CN113313619A - Information pushing method and device, electronic equipment and storage medium - Google Patents

Information pushing method and device, electronic equipment and storage medium Download PDF

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CN113313619A
CN113313619A CN202110349306.8A CN202110349306A CN113313619A CN 113313619 A CN113313619 A CN 113313619A CN 202110349306 A CN202110349306 A CN 202110349306A CN 113313619 A CN113313619 A CN 113313619A
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何颖妮
周春春
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China Construction Bank Corp
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Abstract

The embodiment of the invention discloses an information pushing method, an information pushing device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, wherein the method comprises the following steps: determining a first number of historical service providers corresponding to a current service demander when a service request of the current service demander is received; if the first number is larger than or equal to a preset first number threshold, determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information; determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander; and generating a target push message based on the service providers to be recommended and the matching degree parameter. Through the technical scheme of the embodiment of the invention, the service provider with high matching degree is recommended to the service demander, so that the technical effect of improving the user experience is achieved.

Description

Information pushing method and device, electronic equipment and storage medium
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an information pushing method and device, electronic equipment and a storage medium.
Background
Mutual-support old age is a brand-new old age support mode, which refers to mutual service and help between people in old age support, and the service and the help can be provided for young people or for old people of low age and healthy old people.
At present, all over the country have some regional and small-scale exploration for mutual-aid endowment, but a large-scale and universal mutual-aid endowment service trading platform is not formed. Not only mutual support for the aged, but also the whole aged service field is not perfect in the aspect of intelligent electronization, and only the aged service provider can be recommended for the aged service demander according to the evaluation information, and the aged service provider cannot be recommended individually according to the requirements of the aged service demander, so that the problem that the aged service demander and the aged service provider are not matched can be caused, and even the problem that the user experience degree is poor can be caused.
Disclosure of Invention
The embodiment of the invention provides an information pushing method and device, electronic equipment and a storage medium, and aims to recommend a service provider with high matching degree to a service demander, so that the technical effect of improving the user experience degree is achieved.
In a first aspect, an embodiment of the present invention provides an information pushing method, where the method includes:
determining a first number of historical service providers corresponding to a current service demander when a service request of the current service demander is received;
if the first number is larger than or equal to a preset first number threshold, determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information;
determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander;
and generating a target push message based on the service providers to be recommended and the matching degree parameter.
In a second aspect, an embodiment of the present invention further provides an information pushing apparatus, where the apparatus includes:
the system comprises a first quantity determining module, a second quantity determining module and a third quantity determining module, wherein the first quantity determining module is used for determining a first quantity of historical service providers corresponding to a current service demander when a service request of the current service demander is received;
the approximate demander determining module is used for determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information if the first number is greater than or equal to a preset first number threshold;
the matching degree parameter determining module is used for determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander;
and the target push message generating module is used for generating a target push message based on each service provider to be recommended and the matching degree parameter.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the information pushing method according to any one of the embodiments of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the information pushing method according to any one of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, when a service request of a current service demander is received, a first number of historical service providers corresponding to the current service demander is determined, if the first number is greater than or equal to a preset first number threshold, at least one approximate demander corresponding to the current service demander is determined based on historical service evaluation information, and further, based on the historical service evaluation information of each approximate demander, matching degree parameters of each service provider to be recommended and the current service demander are determined, and a target push message is generated based on each service provider to be recommended and the matching degree parameters.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a flowchart illustrating an information pushing method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an information pushing method according to a second embodiment of the present invention;
fig. 3 is a flowchart illustrating an information pushing method according to a third embodiment of the present invention;
fig. 4 is a flowchart illustrating an information pushing method according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an information pushing apparatus according to a fifth embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of an information pushing method according to an embodiment of the present invention, where the method is applicable to a situation where a service provider with a high matching degree is pushed to a service demander, and the method may be executed by an information pushing apparatus, where the apparatus may be implemented in a form of software and/or hardware, and the hardware may be an electronic device, and optionally, the electronic device may be a mobile terminal, a PC terminal, or the like.
As shown in fig. 1, the method of this embodiment specifically includes the following steps:
s110, when a service request of a current service demander is received, determining a first number of historical service providers corresponding to the current service demander.
The service request may be an instruction or a code for acquiring a service, the service demander may be a user who wants to acquire the service, the service provider may be a user who can provide the service, and the history service provider may be a user who has provided the service for the current service demander. The first number may be the number of historical service providers that have provided service for the current service demander.
Specifically, when the current service demander wants to acquire a service, the service demander can search for a service provider on the platform according to the service requirement. When a service request of a current service demander is received, a historical service record of the current service demander can be acquired from the historical service record, a historical service provider which provides service for the current service demander once can be determined from the historical service record, and the number of the historical service providers is used as a first number.
For example, when the history of the current service demander a records that the service provider B provided 2 times, the service provider C provided 1 time, and the service provider D provided 1 time, the first number may be determined to be 3, that is, the service providers B, C and D.
It should be noted that the service may be a mutual-help endowment service, for example: the service requirements of the senior citizen service demander can be leg-running buying service, door-to-door cleaning service, psychological counseling service and the like. The service may also be other types of services, such as: medical electronic platform services, e-commerce purchasing platform services, and the like.
And S120, if the first number is larger than or equal to a preset first number threshold, determining at least one approximate demander corresponding to the current service demander based on the historical service evaluation information.
The preset first number threshold may be a preset value for determining whether the current service demander is suitable for determining the approximate demander. The historical service evaluation information may be comments and/or scores and the like which are/is given by the service demander to the service provider after the service provider provides services for the service demander. The approximate demander may be a service demander similar to the current service demander, with the understanding that: the approximate demanders of the current service demanders include at least the current service demander.
Specifically, if the first number is greater than or equal to the preset first number threshold, it indicates that the approximate demander can be determined according to the current service demander. For example: the service providers which have common use with the current service demander and the service demanders of which the number of the common use service providers meets the preset requirement can be determined as the approximate demanders, and the service demanders of which the similarity with the basic information of the current service demander reaches the preset requirement can also be determined as the approximate demanders, wherein the basic information can include: age, gender, address, personal preferences, etc.
It should be noted that, if the number of the preliminarily determined approximate demanders is too large, an approximate demander number threshold may be set, and the approximate demander with the approximate demander number threshold may be selected from the preliminarily determined approximate demanders as the finally determined approximate demander.
S130, determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander.
The service provider to be recommended may be a service provider capable of providing a service corresponding to the service request on the platform, or may be a service provider currently in an online state and in an idle state. The matching degree parameter may be a numerical value for measuring the matching degree between the current service demander and each service provider to be recommended.
Specifically, historical service evaluation information of each approximate demander may be obtained, and the service evaluation information may include a service provider that provides services for each approximate demander, and evaluation and/or scoring information that each approximate demander fills in for the service provider. If the historical service evaluation information is comment information, the comment information can be converted into score information. According to the approximate demander and the historical service evaluation information of the approximate demander, the matching degree parameter of each service provider to be recommended and the current service demander can be determined through calculation. For example: the similarity between each approximate demander and the current service demander is used as a weight, the average value of the grading information of each service to be recommended is used as a basic value, the weighted average value corresponding to each service provider to be recommended is obtained by calculation, and the weighted average value is used as a matching degree parameter of each service provider to be recommended and the current service demander; the method may further include obtaining service providers which have provided services for the approximate demanders, determining weights according to the times of the service providers providing services for the approximate demanders, calculating to obtain weighted average values corresponding to the service providers to be recommended by using the calculated average value of the grading information of the service providers as a base value, and using the weighted average values as matching degree parameters of the service providers to be recommended and the current service demanders.
It should be noted that the determination manner of the matching degree parameter may be to comprehensively consider parameter values of each approximate demander and a service provider that provides services for each approximate demander, and the specific calculation method may be specified according to an actual requirement, which is not specifically limited in this embodiment.
It should be further noted that, the converting the comment information into the score information may be: the comment information is determined by extracting keywords in the comment information, or the comment information may be processed by a natural language processing model to obtain score information, and a specific conversion manner is not specifically limited in this embodiment.
And S140, generating a target push message based on each service provider to be recommended and the matching degree parameter.
The target push message may be a message of a recommendation service provider, may include information of each service provider to be recommended and the matching degree parameter, and may also include information of the service providers to be used after the service providers to be recommended are screened according to the matching degree parameter.
Specifically, the service providers to be recommended may be ranked according to the matching degree parameter from high to low, and the ranked service providers to be recommended and the matching degree parameter may be used as a target push message to be pushed to the terminal device of the current service demander for the current service demander to select. And generating a target push message according to the service provider to be recommended, of which the matching degree parameter is greater than the preset matching degree threshold value, so as to push the target push message to the terminal equipment of the current service demander for the current service demander to select.
On the basis of the above embodiments, the target push message is pushed to the terminal device of the current service demander.
Specifically, the target push message is pushed to the terminal device of the current service demander, so that the current service demander can select a service provider meeting the requirement of the current service demander according to the target push message.
On the basis of the above embodiments, the service provider to be recommended is determined according to the service providers in the online state and the idle state.
Specifically, the status may be marked for each service provider, the online status may be a status that the service provider can currently receive orders, the offline status may be a status that the service provider cannot currently receive orders, the idle status may be a status that the service provider does not perform service at the current time, and the busy status may be a status that the service provider is performing service at the current time. According to the states, the service providers to be recommended, which can receive orders in time and complete services in time, can be screened from all the service providers on the platform.
It should be noted that, if a service at a future time is reserved when the service of the service demander is required, the service provider which is in an online state and is not scheduled with a service at the future time may be determined as the service provider to be recommended.
According to the technical scheme of the embodiment of the invention, when a service request of a current service demander is received, a first number of historical service providers corresponding to the current service demander is determined, if the first number is greater than or equal to a preset first number threshold, at least one approximate demander corresponding to the current service demander is determined based on historical service evaluation information, and further, based on the historical service evaluation information of each approximate demander, matching degree parameters of each service provider to be recommended and the current service demander are determined, and a target push message is generated based on each service provider to be recommended and the matching degree parameters.
Example two
Fig. 2 is a flowchart illustrating an information pushing method according to a second embodiment of the present invention, and reference may be made to the technical solution of the present embodiment for a determination method of an approximate requester and a determination method of a matching parameter in the present embodiment based on the foregoing embodiments. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
As shown in fig. 2, the method of this embodiment specifically includes the following steps:
s210, when a service request of a current service demander is received, determining a first number of historical service providers corresponding to the current service demander.
And S220, if the first number is larger than or equal to a preset first number threshold, determining the service demanders with the same historical service providers as the current service demanders and with the second number exceeding the preset second number threshold as approximate demanders corresponding to the current service demanders based on the historical service evaluation information.
Wherein the second number may be the number of service providers that any one service demander has the same history as the current service demander. The preset second number threshold may be a data value used to measure whether the service demander can become the approximate demander corresponding to the current service demander.
It should be noted that the preset first number threshold is usually larger than the preset second number threshold. The preset second number threshold may be set according to platform requirements, and if it is desired to more accurately determine the approximate demander, that is, if the recommended service provider has a higher degree of dependence on the approximate demander, the value of the preset second number threshold may be set to be larger, and if it is desired to more easily determine the recommended service provider, the value of the preset second number threshold may be set to be smaller.
Specifically, if the first number is greater than or equal to a preset first number threshold, the service providers that have provided services for the service demanders are obtained. Further, a second number of service providers having the same history as the current service demander may be determined, and the service demanders having the second number exceeding a preset second number threshold may be determined as the approximate demander corresponding to the current service demander. It can be understood that: a common historical service provider is determined to exist, and the service demanders of which the number exceeds a second number threshold C are determined to be approximate demanders corresponding to the current service demander. Suppose that the current service demander RiWith any service demander RjHistorical service provider set of (1) respectivelyiAnd OjIf | Oi∩OjIf | ≧ C, R can be consideredjIs a reaction with RiCorresponding approximate demander.
Illustratively, if the number of the historical service providers corresponding to the current service demander a is 10, that is, the first number is 10, and exceeds the first number threshold 5, the corresponding approximate demander may be determined for the current service demander a. Assuming that the second number threshold is 3, if the number of historical service providers, which are the same as that of the current service demander a, of the service demander B is 5, that is, the second number is 5, the service demander B may be determined as an approximate demander corresponding to the current service demander a; if the number of the historical service providers, which is the same as that of the current service demander a, of the service demander C is 2, that is, the second number is 2, the service demander C may not be determined to be an approximate demander corresponding to the current service demander a.
It should be noted that, if the preliminarily determined approximate demanders are too many, the threshold of the number of approximate demanders may be preset, the preliminarily determined approximate demanders may be sorted according to the second number, and the preliminarily determined approximate demanders sorted to the previous threshold of the number of approximate demanders may be used as the finally determined approximate demanders.
And S230, determining target reference parameters of the approximate demanders based on the historical service evaluation information of the approximate demanders.
The target reference parameter may be a parameter for measuring the importance of each approximate demander.
Specifically, one or more target reference parameters can be obtained through calculation in a statistical calculation mode or the like according to the historical service evaluation information of each approximate demander, so as to be used for subsequently determining the matching degree parameter of each service provider to be recommended.
And (4) optional. The target reference parameters include: at least one of a similarity parameter, an influence parameter, and a confidence parameter.
The similarity parameter may be a parameter for measuring a degree of similarity between the current service demander and each approximate demander, the influence parameter may be a parameter for measuring an importance degree of each approximate demander in the current service type, and the reliability parameter may be a parameter for measuring a feasibility degree of evaluation information of each approximate demander.
The following respectively describes the specific determination modes of the similarity parameter, the influence parameter and the reliability parameter:
(one) similarity parameter
And determining similarity parameters of the current service demander and each approximate demander based on historical service evaluation information of the current service demander and each approximate demander to the same historical service provider.
Specifically, if the historical service evaluation information of the current service demander and the historical service evaluation information of a certain approximate demander for the same historical service provider are the same or similar, it can be determined that the similarity parameter between the current service demander and the approximate demander is higher. The score information can be determined according to historical service evaluation information of the current service demander and each approximate demander, and similarity parameters of the current service demander and each approximate demander can be determined through a similarity calculation method based on the score information.
It should be noted that the similarity calculation method may be a common distance algorithm, such as euclidean distance, manhattan distance, or the like, or may be a similarity coefficient algorithm, such as cosine similarity, pearson correlation coefficient, or the like, or may be other methods for calculating the similarity, which is not specifically limited in this embodiment.
Optionally, a method for determining similarity parameters between the current service demand party and each approximate demand party is introduced by taking the pearson correlation coefficient as an example.
And determining the Pearson correlation coefficient of the current service demander and each approximate demander based on the historical service evaluation information of the current service demander and each approximate demander to the same historical service provider.
Specifically, the scoring information, that is, the evaluation value may be determined based on historical service evaluation information of the current service demander and each approximate demander on the same historical service provider. The evaluation value can be 0-100, 0-10, etc., and can be set according to the platform requirement.
Determining the Pearson correlation coefficient of the current service demander and each approximate demander based on the following formula
Figure RE-GDA0003161867700000111
Wherein r isijIndicating the current service provider RiAnd any approximate demander RjPearson's correlation coefficient between, IijRepresents RiAnd RjThe same set of historical service providers in diuAnd djuEach represents Ri，RjFor history service provider OuThe evaluation value of (a) of (b),
Figure RE-GDA0003161867700000112
and
Figure RE-GDA0003161867700000113
individual watchShown as Ri,RjIn IijThe above average evaluation value.
And determining similarity parameters of the current service demander and each approximate demander based on the Pearson correlation coefficient.
Specifically, the range of the pearson correlation coefficient is [ -1,1], 1 represents a complete positive correlation, 0 represents an irrelevance, and-1 represents a complete negative correlation, so that if the pearson coefficient is greater than 0, it can be considered that the current service demander is similar to the approximate service demander. The correlation coefficient of the pearson between the current service demander and the approximate demander can be used as a similarity parameter.
Optionally, the specific method for determining the similarity parameter between the current service demander and each approximate demander based on the pearson correlation coefficient may be: if the Pearson correlation coefficient of the current service demand party and the current approximate demand party is larger than zero, determining the Pearson correlation coefficient as a similarity parameter of the current service demand party and the current approximate demand party; and if the Pearson correlation coefficient of the current service demand side and the current approximate demand side is less than or equal to zero, determining that the similarity parameter of the current service demand side and the current approximate demand side is 0.
Specifically, the similarity parameter between the current service demander and each approximate demander can be determined based on the following formula
Figure RE-GDA0003161867700000121
Wherein R isiRepresenting the current service demander, RjRepresenting any of the approximate parties to the demand,
Figure RE-GDA0003161867700000122
indicating the current service provider RiAnd any approximate demander RjThe similarity parameter between, rijIndicating the current service provider RiAnd any approximate demander RjThe pearson correlation coefficient therebetween.
(II) influence force parameter
And determining the sum of the evaluation times of each approximate demander to each service provider in the service demand type based on the historical service evaluation information of each approximate demander, and determining the sum as the sub-evaluation times of each approximate demander.
The service requirement type may be a service type, for example: exercise rehabilitation type, healthy diet type, daily sanitation type, etc.
Specifically, the service demand type of the service required by the current service demander can be determined according to the service request of the current service demander. Further, the number of times that each approximate demander evaluates the service demand type can be determined according to the historical service evaluation information of each approximate demander, that is, the number of times that each service provider obtains the evaluation after providing the service type for the approximate demander is determined.
For example, if the service requirement type of the current service demander a is T, the approximate demander B has been evaluated 3 times by the service provider C of the service requirement type T, 2 times by the service provider D, and 1 time by the service provider E of the service requirement type H, it may be determined that the sum of the evaluation times of the approximate demander B on each service provider in the service requirement type T is 3+2 to 5 times.
It should be noted that, if the service provider provides a service for the service demander, and the service demander does not evaluate the service provider within a preset time, the evaluation information of the service may be set as a default value, such as a default good evaluation, a default full evaluation, and the like, which is not specifically limited in this embodiment.
Based on each sub-evaluation number, a total evaluation number is determined.
Specifically, the total evaluation count may be the sum of all the sub-evaluation counts.
And determining the influence parameters of each approximate demander on the demand service type according to the ratio of each sub-evaluation frequency to the total evaluation frequency.
Specifically, if the service requirement type is T, the influence parameter of each approximate demander on the requirement service type T may be determined according to the following formula
Figure RE-GDA0003161867700000131
Wherein alpha isjRepresenting the approximate square of demand RjInfluence parameter on service type T, approximate Requirements RjThe number of sub-evaluations for service type T is NjThe total evaluation times of all the approximate demanders on the service type T is N.
(III) credibility parameter
And determining the credibility parameters of the approximate demanders based on the sub sum of the times of successful matching of each approximate demander and each service provider and the sum of the times of successful matching of all the approximate demanders and each service provider.
The number of sub-sums may be the number of times that each approximate demander is successfully matched with each service provider, and the sum of the number of times may be the sum of the number of sub-sums of all the approximate demanders. The successful matching may be that the service demander has a good evaluation for the service provider, or that the evaluation value in the evaluation information of the service demander for the service provider satisfies a preset evaluation value threshold, or the like.
Specifically, whether the approximate demanders are successfully matched with the service providers or not is judged according to historical evaluation information corresponding to the approximate demanders, the times of successful matching of the approximate demanders are counted, and the times are used as the times of sub-sums. Further, the sum of the number sub-sums of all the approximate requesters may be used as the number sum. Based on the sum of the times and the sum of the times, the credibility parameters of the approximate demand parties can be calculated.
Optionally, if the historical evaluation information of the approximate demander on the service provider meets a preset satisfactory evaluation condition, determining that the approximate demander and the service provider are successfully matched; and determining the times of successful matching of each approximate demander and each service provider and the sum of the times of successful matching of all the approximate demanders and each service provider.
The preset satisfaction evaluation condition may be a condition for judging the degree of satisfaction of the approximate demander with the service provider. For example: the preset satisfaction evaluation condition can be that a satisfaction option is selected in evaluation, or the scoring information exceeds a scoring threshold value, or the comment information is input into a pre-established natural language processing model, and the obtained satisfaction probability is larger than the dissatisfaction probability.
Optionally, the historical evaluation information includes a historical evaluation value; the historical evaluation information meeting the preset satisfactory evaluation condition comprises the following steps: the historical evaluation value is greater than or equal to a preset satisfactory evaluation threshold value.
Illustratively, the history evaluation information includes a history evaluation value, the evaluation value range of the evaluation value is [1,10], and the preset satisfactory evaluation threshold is 6, that is, when the evaluation value E is greater than or equal to 6, it is determined that the service demander and the service provider are successfully matched. The number of evaluations of the approximate demander a on the service provider B is 3, the historical evaluation values are 8, 5, and 7, respectively, the number of evaluations of the approximate demander a on the service provider C is 2, the historical evaluation values are 3 and 6, respectively, the number of evaluations of the approximate demander a on the service provider D is 1, and the historical evaluation value is 9. At this time, the number of times that the historical evaluation value is greater than or equal to the preset satisfactory evaluation threshold value may be determined to be 4, that is, the sub-sum of the number of times of approximating the demander a may be determined to be 4.
Specifically, after determining the number of sub-sums of successful matching between each approximate demander and each service provider, the sum of all the sub-sums may be processed to obtain a sum value as the sum of the number of times.
And determining the credibility parameter of each approximate demand side according to the ratio of the sum of the times of each approximate demand side to the sum of the times.
Specifically, the confidence parameter for each approximate requester may be determined based on the following formula
Figure RE-GDA0003161867700000151
Wherein A isjRepresenting the approximate square of demand RjA denotes the sum of the degree, βjRepresenting the approximate square of demand RjThe confidence level parameter of (2).
S240, determining the matching degree parameter of each service provider to be recommended based on the target reference parameter and the historical service evaluation information of each approximate demander.
Specifically, the importance degree of each approximate demander can be determined according to the target reference parameter, which may be a weight required for calculating the matching degree parameter. The evaluation information of each service provider to be recommended can be determined based on the historical service evaluation information, and can be scoring information and the like. The score information may be determined as an evaluation value. Further, a weighted sum value of the scoring information of the service provider to be recommended corresponding to each approximate demander can be determined, and the sum value is used as a matching degree parameter of the service provider to be recommended.
Optionally, if the target reference parameter is a similarity parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000152
Wherein,
Figure RE-GDA0003161867700000161
indicating a service provider O to be recommendedkDegree of match parameter, R, with respect to current service demanderiRepresenting the current service demander, RjRepresenting any approximate party to demand, SRiRepresenting an approximate set of demanders, δj(Ok) Representing the approximate square of demand RjService provider O to be recommendedkThe average value of the evaluation values of (a),
Figure RE-GDA0003161867700000162
indicating the current service provider RiAnd approximate demand square RjThe similarity parameter of (2).
It should be noted that, if the service provider O to be recommended is providedkTo approximate the demand side RjOver-service is provided, then deltaj(Ok) Mean value of evaluation values representing these services, if the service provider O is to be recommendedkWithout an approximate demander RjOver-service is provided, then deltaj(Ok) Indicating a service provider O to be recommendedkAnd providing the average value of the evaluation values of the services for all the service demanders.
Optionally, if the target reference parameter is an influence parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000163
Wherein alpha isjRepresenting the approximate square of demand RjThe force-influencing parameter of (c).
Optionally, if the target reference parameter is a reliability parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000164
Wherein, betajRepresenting the approximate square of demand RjThe confidence level parameter of (2).
Optionally, if the target reference parameter is a similarity parameter and an influence parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000165
Optionally, if the target reference parameter is a similarity parameter and a reliability parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000171
Optionally, if the target reference parameter is an influence parameter and a reliability parameter, the matching degree parameter of each service provider to be recommended may be determined based on the following formula
Figure RE-GDA0003161867700000172
Optionally, if the target reference parameter includes a similarity parameter, an influence parameter, and a reliability parameter, the matching degree parameter of each service provider to be recommended is determined based on the similarity parameter, the influence parameter, and the reliability parameter.
Specifically, the matching degree parameter of the service provider to be recommended is determined based on the following formula
Figure RE-GDA0003161867700000173
Wherein,
Figure RE-GDA0003161867700000174
indicating a service provider O to be recommendedkDegree of match parameter, R, with respect to current service demanderiRepresenting the current service demander, RjRepresenting any approximate party to demand, SRiRepresenting an approximate set of demanders, δj(Ok) Representing the approximate square of demand RjService provider O to be recommendedkThe average value of the evaluation values of (a),
Figure RE-GDA0003161867700000175
indicating the current service provider RiAnd approximate demand square RjThe similarity parameter of (a)jRepresenting the approximate square of demand RjInfluence parameter of [ beta ]jRepresenting the approximate square of demand RjThe confidence level parameter of (2).
And S250, generating a target push message based on each service provider to be recommended and the matching degree parameter.
In order to make the generated target push message more meet the requirement of the current service demander, the service provider to be recommended with the highest matching degree parameter may be determined as the service provider to be used, and the target push message is generated according to the service provider to be used.
In order to provide a plurality of service providers for the current service demander for selection, the service provider to be recommended with the matching degree parameter greater than the preset matching degree threshold value may be determined as the service provider to be used, and a target push message may be generated according to the service provider to be used.
The technical scheme of the embodiment of the invention comprises the steps of determining a first number of historical service providers corresponding to a current service demander when receiving a service request of the current service demander, if the first number is greater than or equal to a preset first number threshold, determining a second number of service demanders having the same historical service providers as the current service demander and exceeding a preset second number threshold as approximate demanders corresponding to the current service demander based on historical service evaluation information, further determining a target reference parameter of each approximate demander based on the historical service evaluation information of each approximate demander, determining a matching degree parameter of each service provider to be recommended based on the target reference parameter and the historical service evaluation information of each approximate demander, and generating a target push message based on each service provider to be recommended and the matching degree parameter, the problem that the matching degree of the service demander and the service provider is not high in the prior art is solved, the service provider with high matching degree is recommended for the service demander, and therefore the technical effect of improving the user experience is achieved.
EXAMPLE III
Fig. 3 is a flowchart of an information pushing method according to a third embodiment of the present invention, and in this embodiment, on the basis of the foregoing embodiments, for a case that the first number is smaller than the preset first number threshold, the determination method of the matching degree parameter may refer to the technical solution of this embodiment. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
As shown in fig. 3, the method of this embodiment specifically includes the following steps:
s310, when a service request of a current service demander is received, determining a first number of historical service providers corresponding to the current service demander.
S320, judging whether the first quantity is smaller than a preset first quantity threshold value, if so, executing S330; if not, go to S350.
S330, determining each service provider to be recommended based on the historical service providers, and executing S340.
Specifically, a service provider that has provided services for the current service demander at one time may be used as the service provider to be recommended. If the current service demander does not have a corresponding historical service provider, for example: and if the current service demander is a new user, all service providers can be used as the service providers to be recommended.
And S340, determining a matching degree parameter of each service provider to be recommended based on the historical average evaluation value of each service provider to be recommended, and executing S370.
The historical average evaluation value can be an average value of the evaluation values of the current service demanders to-be-recommended service providers, and the evaluation values can be evaluation information in the historical service evaluation information or evaluation information obtained based on the evaluation information.
Specifically, an average value of the score values of the current service demander to each service provider to be recommended may be used as a historical average evaluation value of each service provider to be recommended, and further, each historical average evaluation value may be used as a matching degree parameter of each service provider to be recommended.
It should be noted that, if the current service demander does not have a corresponding historical service provider, an average value of the credit values of the service providers to be recommended for providing services for all the service demanders may be used as the historical average credit value.
S350, determining at least one approximate demander corresponding to the current service demander based on the historical service evaluation information, and executing S360.
And S360, determining matching degree parameters of each service provider to be recommended and the current service demander based on the historical service evaluation information of each approximate demander, and executing S370.
And S370, generating a target push message based on each service provider to be recommended and the matching degree parameter.
According to the technical scheme of the embodiment of the invention, when the service request of the current service demander is received, the first number of the historical service providers corresponding to the current service demander is determined, if the first number is smaller than the preset first number threshold, each service provider to be recommended is determined based on the historical service providers, further, the matching degree parameter of each service provider to be recommended is determined based on the historical average evaluation value of each service provider to be recommended, and the target push message is generated based on each service provider to be recommended and the matching degree parameter.
Example four
As an optional implementation of the foregoing embodiments, fig. 4 is a schematic flow chart of an information pushing method provided in a fourth embodiment of the present invention. The same or corresponding terms as those in the above embodiments are not explained in detail herein.
As shown in fig. 4, the method of this embodiment is as follows:
let R beiIs a service demander, RiThe service evaluation level formula in the mutual-help endowment platform is
Ei＝[Oi,δi]
Wherein E isiAs a service demander RiService evaluation level (service evaluation information), OiWas to the service demander RiLimited set of service providers, O, offering servicesi＝{Oi1,Oi2,…,OinN denotes a set OiSize of (d), deltaiIs with OiCorresponding service evaluation set, δi(Oik)＝dik，dikRepresenting service demander RiTo service provider OkIs evaluated by k ∈ [1, n ]]。
Assume that the set of service providers is Ord＝[Os,…,Ot]And s and t are subscripts used to distinguish different service providers. When the service provider provides service for the service demander, the historical score is generated, and a historical score mean value set is generated
Figure RE-GDA0003161867700000201
Figure RE-GDA0003161867700000202
Indicating service provider OsThe average of the historical scores of (a),
Figure RE-GDA0003161867700000203
indicating service provider OtThe historical score mean of. When a new service provider joins the mutual-help endowment platform, and the service provider has no historical score, an initial score can be set, such as: 10 minutes, and the like.
Specifically, the input and output of the embodiment of the present invention are introduced:
inputting: (1) service demander RiService evaluation information of [ O ]i,δi]；
(2) Service provider sequence O to be recommendedrd＝[os,…,ot]；
(3) Historical score average of service provider to be recommended
Figure RE-GDA0003161867700000211
(4) The approximate demander impact threshold number C; (first amount)
(5) The approximate number of consumers upper bound K.
And (3) outputting: for service demander RiAnd recommending the service provider with the highest recommendation degree.
Step 1, if the service demand side RiTransacted service provider (historical service provider corresponding to current service demander) | Oi|<C, directly according to the historical average mean value (the historical average evaluation value of each service provider to be recommended)
Figure RE-GDA0003161867700000212
Calculating recommendation degree sequence of service provider (matching degree parameter of each service provider to be recommended)
Figure RE-GDA0003161867700000213
Jumping to the step 6, otherwise, executing the step 2;
step 2, according to the service demand party RiHistorical rating service provider set OiSearching for a sequence of approximate requesters (at least one approximate requester corresponding to a current service requester);
step 3, calculating all approximate demanders and RiFor the evaluation similarity (similarity parameter), the front K-bit approximate demand side with the highest evaluation similarity is selected;
step 4, calculating the evaluation influence (influence parameter) alpha and the evaluation credibility (credibility parameter) beta of the approximate demand party;
step 5, calculating the service provider O to be recommendedrdThe trust recommendation degree (matching degree parameter) of the service provider forms a trust recommendation degree sequence of the service provider
Figure RE-GDA0003161867700000214
Step 6, recommending the degree sequence of the trust of the service provider
Figure RE-GDA0003161867700000215
The service providers with the highest trust and trust recommendation degree are taken as matchers (service providers to be used) from high to low, and the transactions of the supply and demand parties are matched;
and 7, recording the service score after the transaction, and updating the evaluation reliability of the approximate demand party sequence.
According to the technical scheme, the service provider recommendation degree sequence corresponding to the service demander is calculated, and the service provider with the highest trust recommendation degree is taken as the matcher to match the transactions of the supply and demand parties, so that the problem that the service demander and the service provider are not high in matching degree in the prior art is solved, the service provider with high matching degree is recommended for the service demander, and the technical effect of improving the user experience degree is achieved.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an information pushing apparatus according to a fifth embodiment of the present invention, where the apparatus includes: a first number determination module 510, an approximate demander determination module 520, a matching degree parameter determination module 530 and a target push message generation module 540.
The first number determining module 510 is configured to determine, when a service request of a current service demander is received, a first number of historical service providers corresponding to the current service demander; an approximate demander determining module 520, configured to determine, based on historical service evaluation information, at least one approximate demander corresponding to the current service demander if the first number is greater than or equal to a preset first number threshold; a matching degree parameter determining module 530, configured to determine, based on historical service evaluation information of each approximate demander, a matching degree parameter between each service provider to be recommended and the current service demander; a target push message generating module 540, configured to generate a target push message based on the service providers to be recommended and the matching degree parameter.
Optionally, the approximate demander determining module 520 is specifically configured to determine, based on the historical service evaluation information, a service demander whose second number of historical service providers that are the same as the current service demander exceeds a preset second number threshold as an approximate demander corresponding to the current service demander.
Optionally, the matching degree parameter determining module 530 is specifically configured to determine a target reference parameter of each approximate demander based on historical service evaluation information of each approximate demander; and determining the matching degree parameter of each service provider to be recommended based on the target reference parameter and the historical service evaluation information of each approximate demander.
Optionally, the target reference parameters include: at least one of a similarity parameter, an influence parameter, and a confidence parameter.
Optionally, the matching degree parameter determining module 530 is further configured to determine similarity degree parameters between the current service demander and each approximate demander based on historical service evaluation information of the current service demander and each approximate demander on the same historical service provider.
Optionally, the matching degree parameter determining module 530 is further configured to determine, based on historical service evaluation information of the current service demander and each approximate demander on the same historical service provider, a pearson correlation coefficient between the current service demander and each approximate demander; and determining similarity parameters of the current service demander and the approximate demanders based on the Pearson correlation coefficient.
Optionally, the matching degree parameter determining module 530 is further configured to determine the pearson correlation coefficients of the current service demander and the approximate demanders based on the following formula
Figure RE-GDA0003161867700000231
Wherein r isijRepresenting the current service demander RiAnd any of said approximate demander RjPearson's correlation coefficient between, IijRepresents RiAnd RjThe same set of historical service providers in diuAnd djuEach represents Ri， RjFor the history service provider OuThe evaluation value of (a) of (b),
Figure RE-GDA0003161867700000232
and
Figure RE-GDA0003161867700000233
each represents Ri,RjIn IijThe above average evaluation value.
Optionally, the matching degree parameter determining module 530 is further configured to determine, if a pearson correlation coefficient between the current service demander and the current approximate demander is greater than zero, the pearson correlation coefficient as a similarity parameter between the current service demander and the current approximate demander; and if the Pearson correlation coefficient of the current service demand side and the current approximate demand side is less than or equal to zero, determining that the similarity parameter of the current service demand side and the current approximate demand side is 0.
Optionally, the matching degree parameter determining module 530 is further configured to obtain a demand service type of the current service demander, and determine an influence parameter of each approximate demander on the demand service type.
Optionally, the matching degree parameter determining module 530 is further configured to determine, based on historical service evaluation information of each approximate demander, a sum of evaluation times of each approximate demander on each service provider in the service demand type, and determine the sum as a sub-evaluation time of each approximate demander; determining the total evaluation times based on the sub-evaluation times; and determining the influence parameters of each approximate demander on the demand service type according to the ratio of each sub-evaluation frequency to the total evaluation frequency.
Optionally, the matching degree parameter determining module 530 is further configured to determine the credibility parameter of each approximate demander based on a sub-sum of times that each approximate demander and each service provider successfully match and a sum of times that all approximate demanders and each service provider successfully match.
Optionally, the matching degree parameter determining module 530 is further configured to determine that the matching between the approximate demander and the service provider is successful if the historical evaluation information of the approximate demander on the service provider meets a preset satisfactory evaluation condition; and determining the times of successful matching of each approximate demander and each service provider and the sum of the times of successful matching of all the approximate demanders and each service provider.
Optionally, the matching degree parameter determining module 530 is further configured to determine the reliability degree parameter of each approximate demander according to a ratio of the sum of the times of each approximate demander to the sum of the times.
Optionally, the matching degree parameter determining module 530 is further configured to determine a matching degree parameter of each service provider to be recommended based on the similarity parameter, the influence parameter, and the reliability parameter.
Optionally, the matching degree parameter determining module 530 is further configured to determine a matching degree parameter of the service provider to be recommended based on the following formula
Figure RE-GDA0003161867700000241
Wherein,
Figure RE-GDA0003161867700000251
indicating a service provider O to be recommendedkA match factor parameter, R, with respect to the current service demanderiRepresents the current service demander, RjRepresenting any of said approximate demanders, SRiRepresenting an approximate set of demanders, δj(Ok) Representing said approximate demander RjFor the service provider O to be recommendedkThe average value of the evaluation values of (a),
Figure RE-GDA0003161867700000252
representing the current service demander RiAnd the approximate demand square RjThe similarity parameter of (a)jRepresenting said approximate demander RjInfluence parameter of [ beta ]jRepresenting said approximate demander RjThe confidence level parameter of (2).
Optionally, the target push message generating module 540 is specifically configured to determine the service provider to be recommended with the highest matching degree parameter as a service provider to be used, and generate the target push message according to the service provider to be used.
Optionally, the target push message generating module 540 is specifically configured to determine the service provider to be recommended, of which the matching degree parameter is greater than the preset matching degree threshold, as the service provider to be used, and generate the target push message according to the service provider to be used.
Optionally, the apparatus further comprises: and the pushing module is specifically used for pushing the target pushing message to the terminal equipment of the current service demander.
Optionally, the apparatus further comprises: the service provider to be recommended determining module is specifically configured to determine a service provider to be recommended according to a service provider in an online state and an idle state.
Optionally, the apparatus further comprises: the bottom-holding module is specifically used for determining each service provider to be recommended based on the historical service providers if the first number is smaller than a preset first number threshold; and determining the matching degree parameter of each service provider to be recommended based on the historical average evaluation value of each service provider to be recommended.
Optionally, the apparatus further comprises: and the approximate demander updating module is specifically configured to update the approximate demander corresponding to the current service demander based on an evaluation value in the service evaluation information after detecting that the current service demander provides the service evaluation information for the service provider to be recommended.
According to the technical scheme of the embodiment of the invention, when a service request of a current service demander is received, a first number of historical service providers corresponding to the current service demander is determined, if the first number is greater than or equal to a preset first number threshold, at least one approximate demander corresponding to the current service demander is determined based on historical service evaluation information, and further, based on the historical service evaluation information of each approximate demander, matching degree parameters of each service provider to be recommended and the current service demander are determined, and a target push message is generated based on each service provider to be recommended and the matching degree parameters.
The information pushing device provided by the embodiment of the invention can execute the information pushing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
EXAMPLE six
Fig. 6 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary electronic device 60 suitable for use in implementing embodiments of the present invention. The electronic device 60 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 6, the electronic device 60 is in the form of a general purpose computing device. The components of the electronic device 60 may include, but are not limited to: one or more processors or processing units 601, a system memory 602, and a bus 603 that couples various system components including the system memory 602 and the processing unit 601.
Bus 603 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 60 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 60 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 602 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)604 and/or cache memory 605. The electronic device 60 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 606 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 603 by one or more data media interfaces. System memory 602 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 608 having a set (at least one) of program modules 607 may be stored, for example, in system memory 602, such program modules 607 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 607 generally perform the functions and/or methods of the described embodiments of the invention.
Electronic device 60 may also communicate with one or more external devices 609 (e.g., keyboard, pointing device, display 610, etc.), with one or more devices that enable a user to interact with electronic device 60, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 60 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 611. Also, the electronic device 60 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 612. As shown, the network adapter 612 communicates with the other modules of the electronic device 60 via the bus 603. It should be appreciated that although not shown in FIG. 6, other hardware and/or software modules may be used in conjunction with electronic device 60, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602, for example, implementing an information push method provided by an embodiment of the present invention.
EXAMPLE seven
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform an information pushing method, and the method includes:
determining a first number of historical service providers corresponding to a current service demander when a service request of the current service demander is received;
if the first number is larger than or equal to a preset first number threshold, determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information;
determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander;
and generating a target push message based on the service providers to be recommended and the matching degree parameter.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (24)

1. An information pushing method, comprising:
determining a first number of historical service providers corresponding to a current service demander when a service request of the current service demander is received;
if the first number is larger than or equal to a preset first number threshold, determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information;
determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander;
and generating a target push message based on the service providers to be recommended and the matching degree parameter.
2. The method of claim 1, wherein determining at least one approximate demander corresponding to the current service demander based on historical service rating information comprises:
and determining the service demanders with the same historical service providers as the current service demanders and with the second number exceeding a preset second number threshold as the approximate demanders corresponding to the current service demanders based on the historical service evaluation information.
3. The method according to claim 1, wherein the determining the matching degree parameter of each service provider to be recommended and the current service demander based on the historical service evaluation information of each approximate demander comprises:
determining target reference parameters of each approximate demander based on historical service evaluation information of each approximate demander;
and determining the matching degree parameter of each service provider to be recommended based on the target reference parameter and the historical service evaluation information of each approximate demander.
4. The method of claim 3, wherein the target reference parameters comprise: at least one of a similarity parameter, an influence parameter, and a confidence parameter.
5. The method of claim 4, wherein determining the target reference parameter of each approximate demander based on historical service rating information of each approximate demander comprises:
and determining similarity parameters of the current service demander and each approximate demander based on historical service evaluation information of the current service demander and each approximate demander on the same historical service provider.
6. The method of claim 5, wherein determining similarity parameters between the current service demander and each approximate demander based on historical service evaluation information of the current service demander and each approximate demander on the same historical service provider comprises:
determining a Pearson correlation coefficient of the current service demander and each approximate demander based on historical service evaluation information of the current service demander and each approximate demander to the same historical service provider;
and determining similarity parameters of the current service demander and the approximate demanders based on the Pearson correlation coefficient.
7. The method of claim 6, wherein determining the Pearson correlation coefficients for the current service demander and each approximate demander based on historical service rating information for the same historical service provider by the current service demander and each approximate demander comprises:
determining Pearson correlation coefficients for the current service demander and the approximate demanders based on the following formula
Figure FDA0003001944920000021
Wherein r isijRepresenting the current service demander RiAnd any of said approximate demander RjPearson's correlation coefficient between, IijRepresents RiAnd RjThe same set of historical service providers in diuAnd djuEach represents Ri，RjFor the history service provider OuThe evaluation value of (a) of (b),
Figure FDA0003001944920000031
and
Figure FDA0003001944920000032
each represents Ri，RjIn IijThe above average evaluation value.
8. The method of claim 6, wherein said determining a similarity parameter between said current service demander and said each approximate demander based on said pearson correlation coefficients comprises:
if the Pearson correlation coefficient of the current service demand party and the current approximate demand party is larger than zero, determining the Pearson correlation coefficient as a similarity parameter of the current service demand party and the current approximate demand party;
and if the Pearson correlation coefficient of the current service demand side and the current approximate demand side is less than or equal to zero, determining that the similarity parameter of the current service demand side and the current approximate demand side is 0.
9. The method of claim 4, wherein determining the target reference parameter of each approximate demander based on historical service rating information of each approximate demander comprises:
and acquiring the demand service type of the current service demand party, and determining the influence parameters of each approximate demand party on the demand service type.
10. The method of claim 9, wherein determining the impact parameters of the approximate requesters on the demand service type comprises:
determining the sum of the evaluation times of each approximate demander to each service provider in the service demand type based on the historical service evaluation information of each approximate demander, and determining the sum as the sub-evaluation times of each approximate demander;
determining the total evaluation times based on the sub-evaluation times;
and determining the influence parameters of each approximate demander on the demand service type according to the ratio of each sub-evaluation frequency to the total evaluation frequency.
11. The method of claim 4, wherein determining the target reference parameter of each approximate demander based on historical service rating information of each approximate demander comprises:
and determining the credibility parameters of the approximate demanders based on the sub-sum of the times of successful matching of each approximate demander and each service provider and the sum of the times of successful matching of all the approximate demanders and each service provider.
12. The method of claim 11, wherein before determining the credibility parameter of each approximate demander based on the sub-sum of the number of times each approximate demander successfully matched with each service provider and the sum of the number of times each approximate demander successfully matched with each service provider, further comprising:
if the historical evaluation information of the approximate demander to the service provider meets preset satisfactory evaluation conditions, determining that the approximate demander and the service provider are successfully matched;
and determining the times of successful matching of each approximate demander and each service provider and the sum of the times of successful matching of all the approximate demanders and each service provider.
13. The method of claim 11, wherein determining the credibility parameter of each approximate demander based on the sub-sum of the number of times each approximate demander successfully matched with each service provider and the sum of the number of times each approximate demander successfully matched with each service provider comprises:
and determining the credibility parameter of each approximate demand party according to the ratio of the sum of the times of each approximate demand party to the sum of the times.
14. The method according to claim 4, wherein the determining the matching degree parameter of each service provider to be recommended based on the target reference parameter and the historical service evaluation information of each approximate demander comprises:
and determining the matching degree parameter of each service provider to be recommended based on the similarity parameter, the influence parameter and the credibility parameter.
15. The method according to claim 14, wherein the determining a matching degree parameter of each service provider to be recommended based on the similarity parameter, the influence parameter and the reliability parameter comprises:
determining matching degree parameter of service provider to be recommended based on following formula
Figure FDA0003001944920000051
Wherein,
Figure FDA0003001944920000052
indicating a service provider O to be recommendedkA match factor parameter, R, with respect to the current service demanderiRepresents the current service demander,Rjrepresenting any of said approximate demanders, SRiRepresenting an approximate set of demanders, δj(Ok) Representing said approximate demander RjFor the service provider O to be recommendedkThe average value of the evaluation values of (a),
Figure FDA0003001944920000053
representing the current service demander RiAnd the approximate demand square RjThe similarity parameter of (a)jRepresenting said approximate demander RjInfluence parameter of [ beta ]jRepresenting said approximate demander RjThe confidence level parameter of (2).
16. The method according to claim 1, wherein the generating a targeted push message based on the service providers to be recommended and the matching degree parameter comprises:
and determining the service provider to be recommended with the highest matching degree parameter as a service provider to be used, and generating a target push message according to the service provider to be used.
17. The method according to claim 1, wherein the generating a targeted push message based on the service providers to be recommended and the matching degree parameter comprises:
and determining the service provider to be recommended with the matching degree parameter larger than a preset matching degree threshold value as a service provider to be used, and generating a target push message according to the service provider to be used.
18. The method according to claim 1, further comprising, after the generating a targeted push message based on the service providers to be recommended and the matching degree parameter:
and pushing the target push message to the terminal equipment of the current service demand party.
19. The method of claim 1, further comprising:
and determining the service provider to be recommended according to the service providers which are in the online state and the idle state.
20. The method of claim 1, further comprising:
if the first number is smaller than a preset first number threshold, determining each service provider to be recommended based on the historical service providers;
and determining the matching degree parameter of each service provider to be recommended based on the historical average evaluation value of each service provider to be recommended.
21. The method of claim 1, further comprising:
and after the current service demander is detected to provide service evaluation information for the service provider to be recommended, updating the approximate demander corresponding to the current service demander based on the evaluation value in the service evaluation information.
22. An information pushing apparatus, comprising:
the system comprises a first quantity determining module, a second quantity determining module and a third quantity determining module, wherein the first quantity determining module is used for determining a first quantity of historical service providers corresponding to a current service demander when a service request of the current service demander is received;
the approximate demander determining module is used for determining at least one approximate demander corresponding to the current service demander based on historical service evaluation information if the first number is greater than or equal to a preset first number threshold;
the matching degree parameter determining module is used for determining matching degree parameters of each service provider to be recommended and the current service demander based on historical service evaluation information of each approximate demander;
and the target push message generating module is used for generating a target push message based on each service provider to be recommended and the matching degree parameter.
23. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the information push method of any one of claims 1-21.
24. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the information push method according to any one of claims 1 to 21.
CN202110349306.8A 2021-03-31 2021-03-31 Information pushing method and device, electronic equipment and storage medium Pending CN113313619A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114048373A (en) * 2021-10-21 2022-02-15 盐城金堤科技有限公司 Recommendation method, device, medium and electronic equipment for service organization

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114048373A (en) * 2021-10-21 2022-02-15 盐城金堤科技有限公司 Recommendation method, device, medium and electronic equipment for service organization

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