CN113129050A - Data object recommendation method and device - Google Patents
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Abstract
The application relates to a data object recommendation method and device. The method comprises the following steps: acquiring identification information of a first data object added to an object list from a first data source; under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information; recommending the second data object. By using the data object recommendation method and device provided by the embodiments of the application, the process of adding the data object from the data source can be simplified.
Description
Technical Field
The present application relates to the field of data processing technologies for data objects, and in particular, to a data object recommendation method and apparatus.
Background
Currently, Online to Offline (O2O) technology is widely used in people's daily life, and users can take orders Online or Offline or deliver goods to home by delivery personnel. Typical application scenarios include point of sale, drug delivery to the home, fresh flower delivery, and the like. In the O2O technology, a user is often assigned a commodity from a store located closest to the user based on the user's location or shipping address. However, if the user switches the positioning or the shipping address, the store closest to the user needs to be newly selected. If the user has added some items to the shopping list at the previous store, in the case where the store visited by the user jumps to another store, it is often necessary for the user to re-search for items from the store and add items to the shopping list again. This may be cumbersome for the user, and may require a significant amount of time and effort for the user, especially when the number of products purchased in the previous store is high.
Therefore, there is a need in the related art for a way to facilitate a user to purchase a commodity in the case of changing stores in the O2O technology.
Disclosure of Invention
The embodiment of the application aims to provide a data object recommendation method and device, which can simplify the process of adding data objects.
The data object recommendation method and device provided by the embodiment of the application are realized as follows:
a method of data object recommendation, the method comprising:
acquiring identification information of a first data object added to an object list from a first data source;
under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information;
recommending the second data object.
A data object recommendation apparatus comprising a processor and a memory for storing processor-executable instructions that when executed by the processor implement:
acquiring identification information of a first data object added to an object list from a first data source;
under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information;
recommending the second data object.
A non-transitory computer readable storage medium having instructions that, when executed by a processor, enable the processor to perform the data object recommendation method.
The data object recommendation method and device provided by the application can recommend the data object matched with the data object in the first data source before switching in the second data source after switching in the process of switching the data sources. Therefore, the user does not need to search the required data object again in the switched second data source, and the process of adding the data object is simplified. The data object recommendation method is particularly suitable for application scenes of commodity purchasing in different stores, book borrowing in different libraries and the like, and brings convenience to scenes of commodity purchasing, book borrowing and the like.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flowchart illustrating a data object recommendation method according to an example embodiment.
FIG. 2 is a flowchart illustrating a data object recommendation method according to an example embodiment.
FIG. 3 is a diagram illustrating an application scenario in accordance with an exemplary embodiment.
FIG. 4 is a diagram illustrating an application scenario in accordance with an exemplary embodiment.
FIG. 5 is a diagram illustrating an application scenario in accordance with an exemplary embodiment.
FIG. 6 is a diagram illustrating an application scenario in accordance with an exemplary embodiment.
FIG. 7 is a diagram illustrating an application scenario in accordance with an exemplary embodiment.
FIG. 8 is a flowchart illustrating a data object recommendation method according to an example embodiment.
FIG. 9 is a block diagram illustrating a data object recommendation device according to an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
For the convenience of those skilled in the art to understand the technical solutions provided in the embodiments of the present application, the following description is made through several application scenarios.
Fig. 1 is a diagram showing a relationship between different entities involved in an application scenario, and fig. 2 is a diagram showing a plurality of stores such as a store a and a store B registered on a platform in fig. 1 according to the present application as shown in fig. 1, and a user can enter each store through the platform to shop. As shown in fig. 2, the user may input a search keyword in the platform, in this scenario, the user inputs the keyword "cold", and the platform determines that the user needs to be provided with a medication service according to the search keyword of the user, and selects a store a closest to the user-located position from a plurality of stores as a store for distributing medicines to the user. The user receives the recommendation of the platform, can enter the store a, and add four products from the store a to the shopping list, fig. 3 is a user interface of the shopping list, as shown in fig. 3, the user adds one product a, one product B, and two products C to the shopping list, the name, cost, purchase amount, and the like of each product are shown in the shopping list, and of course, items such as total price, distribution fee, and the like can also be shown, which are not shown in the figure. For some products on the platform, especially pharmaceutical products, a standard database shown in fig. 1 may be provided, and the standard database may be used to store a plurality of standard product information, and specifically, reference may be made to the example shown in table 1. For example, when a shop is at a new medicine a, the store can search whether the medicine a exists in the standard database, if so, the medicine a can be put on the shelf from the standard database, and information such as a title, a product drawing, a product description and the like unified in the standard database can be used, so that the product can be put on the shelf quickly.
However, the user may need to switch the shipping address, for example, switching the shipping address from a company to a home, or switching the location, and the platform may automatically switch the store currently visited by the user to the store closest to the address switched by the user, for example, switching the store to the store 2. As shown in step 6 of fig. 2, it is possible to determine whether or not the stock of all the products purchased in the store 1 is satisfied in the store 2. Specifically, it is possible to inquire whether or not the same product as the product purchased in the store 1 exists in the store 2 based on the general information of the product purchased in the store 1. If all the products purchased at store A are in stock at store B, the procedure shown in step 7 or step 8 may be followed. The same product as the product purchased in the store 1 may be automatically added to the shopping list, as shown in step 7. FIG. 4 is a shopping list interface diagram of the store 2, as shown in FIG. 4, where products A, B, C may be added to the shopping list. Further, since the transaction costs of selling the same product in each store are different from each other, if the transaction costs of the store 2 are different from the store 1, cost change information may be added near the transaction costs of the store 2, for example, the cost of the product a in the store 2 is 1.1 lower than that of the store 1, and the cost of the product B in the store 2 is 0.4 higher than that of the store 1, so that the change value of the cost and the smiling face expression may be set near the product cost in the shopping list of the store 2 shown in fig. 4, and an unfair expression may be set for the product B, and by this setting, the user can quickly know whether the product cost is rising or falling.
Of course, as shown in fig. 5, the product A, B, C in the store 2 may be set as an optional object, that is, the user may freely select whether to add the product to the shopping list, such as "all buy" or "temporarily not buy", and so on.
In other scenarios, there may be no products in store 2 that are the same as those purchased in store 1, based on which, however, there may be products in store 2 that are similar to those purchased in store 1. Then, the similar product can also be recommended to the user, fig. 6 is a user interface diagram for recommending products, and as shown in fig. 6, the same product and the similar product can be displayed in different areas, such as displaying product a and product B in the upper half area of the page, and displaying product D similar to product C in the lower half area of the page. By this way, the user can quickly know which products are the same in the shopping list in the shop 1 and which are similar products.
In another embodiment, such as the user interface of the recommended product shown in FIG. 7, the recommended product may also be divided into three display areas, wherein the uppermost area is used to display the same products as purchased in the store 1, namely product A and product B. The middle area is used to display a product similar to the product purchased in store 1, product D. The lowest area is used for displaying some products which are matched with the product A or the product B in function, and if the matching of a plurality of products can produce good effect according to medical principles or use experience, the products which are matched in function can be recommended to the user. For example, in the case where the user purchases iodine, a cotton swab may be recommended to the user. As shown in fig. 7, according to the experience of use, the product a can have better therapeutic effect when used with the product E, and based on this, such products can be displayed in one of the sections of the user interface and accompanied by corresponding prompt information, such as "better match with ganmaoling granules" in fig. 7.
The data object recommendation method described in the present application is described in detail below with reference to the accompanying drawings. Fig. 8 is a flowchart illustrating a method according to an embodiment of a data object recommendation method provided in the present application. Although the present application provides method steps as shown in the following examples or figures, more or fewer steps may be included in the method based on conventional or non-inventive efforts. In the case of steps where no necessary causal relationship exists logically, the order of execution of the steps is not limited to that provided by the embodiments of the present application. The method can be executed sequentially or in parallel (for example, in the context of a parallel processor or a multi-thread process) according to the method shown in the embodiment or the figures when the method is executed in an actual data object recommendation process or device.
Specifically, an embodiment of the data object recommendation method provided by the present application is shown in fig. 8, where the method may include:
s801: identification information of a first data object added to the object list from a first data source is obtained.
S803: and under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information.
S805: recommending the second data object.
In the embodiment of the present application, the data object may include an object that can be recommended to a user. For example, in an application scenario of recommending commodities to users, the data objects may include commodity information, the corresponding data sources may include shops selling commodities, and the object list may include shopping lists used for temporarily storing the commodity information in the clients. The data object recommendation can also be applied to other scenes, such as article renting and the like, such as book borrowing and the like, based on the data object recommendation, the data object can comprise book information, the corresponding data source can comprise a library and other objects capable of borrowing books, and the object list can comprise a book list of the book information temporarily stored by the user in the client. Of course, in other embodiments, the data object may include any object that can be recommended to a user and that has different data sources available, and the present application is not limited thereto.
In the embodiment of the application, in the process of accessing the first data source, the user may add a data object from the first data source to the object list, where the object list may include a container for temporarily storing the data object, and the data object in the object list may be deleted, modified, or, of course, may also be purchased through settlement. It should be noted that the data object described in the present application is an access object of an online purchasing entity data object. Regarding the application environment in the embodiments of the present application, in some e-commerce platforms, multiple data sources are often registered, and many goods sold by each data source are duplicated, but each data source has independent attributes, for example, customization of goods price, preferential event, location of data object, and the like. For example, for a platform-based delivery service, the platform would default to delivering drugs from the pharmacy closest to the user's location from which the user selected the data object and added the data object to the list of objects. However, the user suddenly finds that the order made by the user in the company is sent to the company, but actually needs to be sent to the home, so that the data object receiving address is modified to be the address of the home, and at the moment, the platform switches the pharmacy to the pharmacy closest to the address of the home of the user. The pharmacy may be the second data source according to the embodiment of the present application, and in the embodiment of the present application, a second data object matching the first data object in the second data source may be determined from the second data source, and then the second data object is recommended to the user.
In this embodiment of the present application, in the process of determining, from the second data source, the second data object that matches the first data object, the identification information of the first data object may be obtained. The way of matching the first data object and the second data object may include:
the data object information of the first data object and the second data object is the same;
or the similarity of the data object information of the first data object and the second data object is greater than a preset similarity threshold.
Wherein the data object information may include at least one of: data object name, data object specification, data object composition, data object brand, data object function, data object producer, and the like.
In practical applications, there may be no second data object in the second data source that is identical to the first data object, and then, further, a second data object that is similar to the first data object may be recommended. The similarity between the first data object and the second data object needs to be calculated. In one embodiment, the similarity between the first data object and the second data object may be calculated from data object descriptions of the first data object and the second data object. In an embodiment of the present application, data object descriptions of the first data object and the second data object may be obtained respectively. The data object description may include information about the data object that the data object provides when entered into the platform. In one example, for a drug, according to the regulations of the chinese country, it is necessary to have a drug instruction book, and the drug instruction book needs to include indispensable parts of drug information such as drug name, ingredients, properties, functional indications, specifications, usage amount, adverse reactions, contraindications, storage, packaging, expiration date, and the like. Based on this, when calculating the similarity between different medicines, the calculation may be performed using the specifications of the medicines, and of course, information that can embody the functions of the medicines in the specifications of the medicines may be selected as data for calculating the similarity between the medicines, for example, the functional indications in the specifications of the medicines.
In an actual application environment, the data object description is often a text paragraph, and based on this, in the embodiment of the present application, in the process of calculating the similarity between the data objects by using the data object description, a keyword may be extracted from the data object descriptions of the first data object and the second data object. In one embodiment, the keywords may include functional keywords, specification keywords, and the like. Of course, before extracting the keywords, data preprocessing may be performed on the data object description, such as removing useless words, and the like, and word segmentation may be performed on the data object description, and the keywords are extracted from the word segmentation. In one example, keywords extracted from the drug specification for drug a include "cold", "fever", "ease of pain", "nasal congestion", "runny nose", and the like, and keywords extracted from the drug specification for drug B include "headache", "fever", "sore throat", "fever", and the like. Then, the keywords corresponding to the first data object and the second data object respectively may be merged into a keyword set, and the word frequency of each keyword in the keyword set in the data object descriptions of the first data object and the second data object may be counted respectively. In the merging process, processing such as merging synonyms can be performed on the keywords. For example, in the keywords of the a medicine and the B medicine, the terms "fever" and "fever" are synonymous, and the two terms may be combined to "fever" or "fever". Based on this, the combined keyword set includes the keywords "cold", "fever", "pain", "nasal obstruction", "running nose", "headache", "sore throat", and the like. And counting to obtain the word frequency of each keyword in the keyword set appearing in the medicine A as 5,3,3,1,1,1,0,0, and counting to obtain the word frequency of each keyword in the keyword set appearing in the medicine B as 0,5,0,0,0,0,4, 3. And then, determining the similarity between the medicine A and the medicine B according to the word frequency data. In one embodiment, word frequency vectors corresponding to the first data object and the second data object may be respectively constructed according to the word frequency data, and then, cosine values between the word frequency vectors corresponding to the first data object and the second data object are calculated and taken as similarities between the first data object and the second data object. In the above example, it may be constructed that vector a is (5,3,3,1,1,1,0,0), vector B is (0,5,0,0,0,0,4,3), a cosine value between vector a and vector B is calculated, and the cosine value is taken as a similarity between a medicine and B medicine. Based on this, the closer the cosine value is to 1, the greater the similarity between the a medicine and the B medicine. Of course, in other embodiments, the difference between the modulus values of the vectors may also be taken into account to calculate a more accurate similarity between a and B drugs.
In the embodiment of the present application, a plurality of sets of identical data objects may also be pre-constructed, and stored in the first database. The data objects contained in the same data object set have the same data object information. In one example, for a pharmaceutical class data object, a corresponding pharmaceutical product set may be constructed for the same pharmaceutical product, and the pharmaceutical products in the pharmaceutical product set may be from different drug stores, and the structural form of the same data object set is exemplarily illustrated in table 1 below. The drug standard product database shown in table 1 may include three columns of data, namely a standard product name & ID, a drug ID, and a data source ID, where the standard product name is a common name of the same drug in the drug standard product database, and the standard product name may also correspond to an ID value, that is, a unique identifier of the drug set in the drug standard product database, and the ID may be used to search for a data source selling the drug and a unique identifier of the drug set on the platform. The following describes how, by way of an example, after the medicine yunnan white drug bandage of the first data source with the data source ID of "3479890" is obtained by using the medicine label database, the identification information of the medicine, namely "497980979", can be obtained. According to the identification information, the identification information of the medicine set corresponding to the medicine can be determined to be 'ID-J7699'. If the identification information of the switched second data source is "8793897", by querying the drug ID and the data source ID corresponding to the yunnan white drug woundplast, it can be determined that the second data object with the identification information of "027237489" exists in the second data source with the identification information of "8793897", and the second data object can be recommended.
The same data objects of different data sources can be arranged in the same set through the pre-constructed first database, so that whether the same second data object exists in the second data source can be quickly judged through the first data object of the first data source, and if the same second data object exists, the second data object can be recommended to a user.
TABLE 1 drug Standard database
In an actual application environment, a second data object which is identical to the first data object does not necessarily exist in the second data source, and therefore, a second database may be further established for storing a plurality of sets of similar data objects, where the similarity between the data object information of the data objects included in the sets of similar data objects is greater than a preset similarity threshold. The calculation method of the similarity may refer to the implementation methods of the above embodiments, and details are not repeated herein. The specific manner of constructing the second database may refer to the manner in table 1, and is not described herein again. Of course, the priority of the same data object set may be higher than the priority of the similar data object set, and in a case that it is determined that the second data object of the second data source does not exist in the same data object set, the similar data object set corresponding to the identification information may be searched from the second database, and it may be determined whether the second data object of the second data source exists in the similar data object set. In the event that a determination is made that there is a match, it may be determined that the second data object matches the first data object. Otherwise, in a case that it is determined that the similar data object set corresponding to the identification information does not exist in the second database or a second data object of the second data source does not exist in the similar data object set, it may be determined that a second data object matching the first data object does not exist in the second data source.
In the embodiment of the application, in the process of recommending the second data object to the user, the second data object may be added to the object list, and of course, the second data object may also be set as a selectable object and information of the second data object is displayed, so that the user may freely select that the second data object does not need to be added to the object list. In practical application, for the same data object, the transaction costs set by different data sources are different, and the transaction costs are the costs that the user needs to pay to acquire the data object. Then, if the transaction cost of the second data object relative to the first data object in the second data source changes, change information, such as a transaction cost increase or decrease and the number of increases or decreases, may be obtained. Information of the second data object and the change information of the transaction cost, such as a change value of the transaction cost, may then be presented in a user interface. The change information may be expressed in an emotional form, for example, in an expression pattern of an active type such as "happy", "smiling", or "laugh" when the transaction cost falls, or in an expression pattern of a sad type such as "sad" or "crying" when the transaction cost rises.
In one embodiment of the present application, in the recommending the second data object, in a case where the first data object includes a plurality of data objects and the second data source includes a second data object that is the same as and similar to data object information of the plurality of first data objects, the second data object that is the same as the data object information of the first data object is presented by using the first area, and the second data object that is similar to the data object information of the first data object is presented by using the second area. In one example, the user adds 4 data objects to the object list in the first data source, and after the user changes the shipping address, the accessed data source is switched to the second data source, from which the same data object as the first data object and two similar data objects are determined in the manner of the above-described embodiment. Then two presentation areas, a first area for presenting the same data objects and a second area for presenting similar data objects, may be provided in the user interface. By means of displaying the data objects in the subareas, the user can be helped to know which recommended data objects are the same data objects and which recommended data objects are similar data objects, and reference is provided for the user to add the data objects into the object list. Of course, in other embodiments, the same data object may be added to the object list, and the similar data object may be set as a selectable object and displayed to the user, so that the user may freely select whether the similar data object needs to be added to the object list.
Further, in an embodiment of the present application, the manner in which the first data object and the second data object are matched further includes: a data object that said first data object matches functionally in use with said second data object. The functionally matching may include that two or more data objects, when used in combination, produce better results than when one of the data objects is used alone, based on usage principles or experience. For example, iodine tincture can produce better effect when used with cotton swab, and Ganmaoling granule can produce better heat-clearing and detoxicating effect when used with Liushen pill. Based on this, a third area can be set in the user interface for showing the second data object functionally matching with the first data object, so as to help the user to make a better choice.
It should be noted that the display mode of the second data object is not limited to the above example, and those skilled in the art may make other changes within the spirit of the present application, but the present application is covered by the scope of protection as long as the achieved function and effect are the same or similar to the present application.
The data object recommendation method provided by the application can recommend the data object matched with the data object in the first data source before switching in the second data source after switching in the process of switching the data sources. Therefore, the user does not need to search the required data object again in the switched second data source, and the process of adding the data object is simplified. The data object recommendation method is particularly suitable for application scenes of commodity purchasing in different stores, book borrowing in different libraries and the like, and brings convenience to scenes of commodity purchasing, book borrowing and the like.
Corresponding to the data object recommendation method, as shown in fig. 9, the present application further provides a data object recommendation device, including a processor and a memory for storing processor-executable instructions, where the processor, when executing the instructions, may implement:
acquiring identification information of a first data object added to an object list from a first data source;
under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information;
recommending the second data object.
Optionally, in an embodiment of the present application, when implementing the step of matching the first data object with the second data object, the processor includes:
the data object information of the first data object and the second data object is the same;
or the similarity of the data object information of the first data object and the second data object is greater than a preset similarity threshold.
Optionally, in an embodiment of the present application, the accessing the second data source includes:
and detecting that the positioning position or the data object receiving address changes, wherein the distance between the address of the second data source and the new positioning position or the data object receiving address is smaller than a preset distance threshold value.
Optionally, in an embodiment of the application, when the processor determines, according to the identification information, a second data object in the second data source that matches the first data object, the implementing step includes:
searching a same data object set corresponding to the identification information from a first database, wherein the first database is used for storing a plurality of same data object sets, and data objects contained in the same data object sets have the same data object information;
judging whether a second data object of the second data source exists in the same data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
Optionally, in an embodiment of the application, after the determining, by the processor, whether a second data object of the second data source exists in the same data object set, the method further includes:
under the condition that the data object information does not exist, searching a similar data object set corresponding to the identification information from a second database, wherein the second database is used for storing a plurality of similar data object sets, and the similarity between the data object information of the data objects contained in the same similar data object set is greater than a preset similarity threshold;
judging whether a second data object of the second data source exists in the similar data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
Optionally, in an embodiment of the present application, the data object information includes at least one of the following: data object name, data object specification, data object composition, data object brand, data object function, data object producer.
Optionally, in an embodiment of the present application, the similarity between the first data object and the second data object is set to be calculated as follows:
respectively acquiring data object descriptions of the first data object and the second data object;
extracting keywords from the data object descriptions of the first data object and the second data object respectively;
combining the keywords respectively corresponding to the first data object and the second data object into a keyword set, and respectively counting the word frequency of each keyword in the keyword set in the data object descriptions of the first data object and the second data object;
and determining the similarity between the first data object and the second data object according to the corresponding word frequency of the first data object and the second data object.
Optionally, in an embodiment of the application, when the processor determines the similarity between the first data object and the second data object according to the word frequencies corresponding to the first data object and the second data object in the implementation step, the processor includes:
respectively constructing word frequency vectors corresponding to the first data object and the second data object;
and calculating cosine values between the word frequency vectors corresponding to the first data object and the second data object, and taking the cosine values as the similarity between the first data object and the second data object.
Optionally, in an embodiment of the present application, the processor, when implementing the step of recommending the second data object, includes:
adding the second data object to the object list.
Optionally, in an embodiment of the present application, the processor, when implementing the step of recommending the second data object, includes:
and setting the second data object as a selectable object and displaying the information of the second data object.
Optionally, in an embodiment of the present application, the processor, when implementing the step of recommending the second data object, includes:
determining change information of a transaction cost of the second data object relative to the first data object;
and displaying the information of the second data object and the change information of the transaction cost.
Optionally, in an embodiment of the present application, the change information is configured to be expressed in an emotional manner.
Optionally, in an embodiment of the present application, the processor, when implementing the step of recommending the second data object, includes:
in the case that the first data object includes a plurality of data objects and the second data source includes a second data object that is the same as and similar to the data object information of the plurality of first data objects, the second data object that is the same as the data object information of the first data object is presented using the first area, and the second data object that is similar to the data object information of the first data object is presented using the second area.
Optionally, in an embodiment of the present application, the manner of matching the first data object with the second data object further includes:
a data object that said first data object matches functionally in use with said second data object.
Optionally, in an embodiment of the present application, the processor, when implementing the step of recommending the second data object, includes:
in case the first data object comprises a plurality of and the second data origin comprises a second data object being identical, similar and functionally matching to the data object information of a plurality of the first data objects, presenting the second data object being identical to the data object information of the first data object with a first area, presenting the second data object being similar to the data object information of the first data object with a second area, presenting the second data object being functionally matching to the first data object with a third area.
In another aspect, the present application further provides a computer-readable storage medium, on which computer instructions are stored, and the instructions, when executed, implement the steps of the method according to any of the above embodiments.
The computer readable storage medium may include physical means for storing information, typically by digitizing the information for storage on a medium using electrical, magnetic or optical means. The computer-readable storage medium according to this embodiment may include: devices that store information using electrical energy, such as various types of memory, e.g., RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, tapes, core memories, bubble memories, and usb disks; devices that store information optically, such as CDs or DVDs. Of course, there are other ways of storing media that can be read, such as quantum memory, graphene memory, and so forth.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a data object having a certain function. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program data object. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program data object embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program data objects according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program data object. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program data object embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (31)
1. A method for data object recommendation, the method comprising:
acquiring identification information of a first data object added to an object list from a first data source;
under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information;
recommending the second data object.
2. The data object recommendation method of claim 1, wherein the manner in which the first data object is matched to the second data object comprises:
the data object information of the first data object and the second data object is the same;
or the similarity of the data object information of the first data object and the second data object is greater than a preset similarity threshold.
3. The data object recommendation method of claim 1, wherein said accessing a second data source comprises:
and detecting that the positioning position or the data object receiving address changes, wherein the distance between the address of the second data source and the new positioning position or the data object receiving address is smaller than a preset distance threshold value.
4. The method according to claim 1, wherein the determining a second data object in the second data source that matches the first data object according to the identification information comprises:
searching a same data object set corresponding to the identification information from a first database, wherein the first database is used for storing a plurality of same data object sets, and data objects contained in the same data object sets have the same data object information;
judging whether a second data object of the second data source exists in the same data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
5. The method of claim 4, wherein after the determining whether a second data object from the second data source exists in the same set of data objects, the method further comprises:
under the condition that the data object information does not exist, searching a similar data object set corresponding to the identification information from a second database, wherein the second database is used for storing a plurality of similar data object sets, and the similarity between the data object information of the data objects contained in the same similar data object set is greater than a preset similarity threshold;
judging whether a second data object of the second data source exists in the similar data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
6. The data object recommendation method of claim 4 or 5, wherein the data object information comprises at least one of: data object name, data object specification, data object composition, data object brand, data object function, data object producer.
7. The data object recommendation method of claim 2, wherein the similarity between the first data object and the second data object is arranged to be calculated as follows:
respectively acquiring data object descriptions of the first data object and the second data object;
extracting keywords from the data object descriptions of the first data object and the second data object respectively;
combining the keywords respectively corresponding to the first data object and the second data object into a keyword set, and respectively counting the word frequency of each keyword in the keyword set in the data object descriptions of the first data object and the second data object;
and determining the similarity between the first data object and the second data object according to the corresponding word frequency of the first data object and the second data object.
8. The method of claim 7, wherein determining the similarity between the first data object and the second data object according to the word frequencies corresponding to the first data object and the second data object comprises:
respectively constructing word frequency vectors corresponding to the first data object and the second data object;
and calculating cosine values between the word frequency vectors corresponding to the first data object and the second data object, and taking the cosine values as the similarity between the first data object and the second data object.
9. The data object recommendation method of claim 1, wherein said recommending the second data object comprises:
adding the second data object to the object list.
10. The data object recommendation method of claim 1, wherein said recommending the second data object comprises:
and setting the second data object as a selectable object and displaying the information of the second data object.
11. The data object recommendation method of claim 1, wherein said recommending the second data object comprises:
determining change information of a transaction cost of the second data object relative to the first data object;
and displaying the information of the second data object and the change information of the transaction cost.
12. The data object recommendation method of claim 11, wherein the change information is arranged to be expressed in an emotionalized manner.
13. The data object recommendation method of claim 2, wherein said recommending the second data object comprises:
in the case that the first data object includes a plurality of data objects and the second data source includes a second data object that is the same as and similar to the data object information of the plurality of first data objects, the second data object that is the same as the data object information of the first data object is presented using the first area, and the second data object that is similar to the data object information of the first data object is presented using the second area.
14. The data object recommendation method of claim 2, wherein the manner in which the first data object is matched to the second data object further comprises:
a data object that said first data object matches functionally in use with said second data object.
15. The data object recommendation method of claim 14, wherein said recommending the second data object comprises:
in case the first data object comprises a plurality of and the second data origin comprises a second data object being identical, similar and functionally matching to the data object information of a plurality of the first data objects, presenting the second data object being identical to the data object information of the first data object with a first area, presenting the second data object being similar to the data object information of the first data object with a second area, presenting the second data object being functionally matching to the first data object with a third area.
16. A data object recommendation device comprising a processor and a memory for storing processor-executable instructions, the instructions when executed by the processor implementing:
acquiring identification information of a first data object added to an object list from a first data source;
under the condition of accessing a second data source, determining a second data object matched with the first data object in the second data source according to the identification information;
recommending the second data object.
17. The data object recommendation device of claim 16, wherein the processor, in implementing the manner of matching the first data object with the second data object, comprises:
the data object information of the first data object and the second data object is the same;
or the similarity of the data object information of the first data object and the second data object is greater than a preset similarity threshold.
18. The data object recommendation device of claim 16, wherein said accessing a second data source comprises:
and detecting that the positioning position or the data object receiving address changes, wherein the distance between the address of the second data source and the new positioning position or the data object receiving address is smaller than a preset distance threshold value.
19. The data object recommendation device of claim 16, wherein the processor, when performing the step of determining a second data object from the second data source that matches the first data object based on the identification information, comprises:
searching a same data object set corresponding to the identification information from a first database, wherein the first database is used for storing a plurality of same data object sets, and data objects contained in the same data object sets have the same data object information;
judging whether a second data object of the second data source exists in the same data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
20. The data object recommendation device of claim 19, wherein after the processor determines whether a second data object from the second data source exists in the same set of data objects, the processor further comprises:
under the condition that the data object information does not exist, searching a similar data object set corresponding to the identification information from a second database, wherein the second database is used for storing a plurality of similar data object sets, and the similarity between the data object information of the data objects contained in the same similar data object set is greater than a preset similarity threshold;
judging whether a second data object of the second data source exists in the similar data object set or not;
in the event that a determination is made that there is a match, the second data object is determined to match the first data object.
21. The data object recommendation device of claim 19 or 20, wherein the data object information comprises at least one of: data object name, data object specification, data object composition, data object brand, data object function, data object producer.
22. The data object recommendation device of claim 17, wherein the similarity between the first data object and the second data object is arranged to be calculated as follows:
respectively acquiring data object descriptions of the first data object and the second data object;
extracting keywords from the data object descriptions of the first data object and the second data object respectively;
combining the keywords respectively corresponding to the first data object and the second data object into a keyword set, and respectively counting the word frequency of each keyword in the keyword set in the data object descriptions of the first data object and the second data object;
and determining the similarity between the first data object and the second data object according to the corresponding word frequency of the first data object and the second data object.
23. The data object recommendation device of claim 22, wherein the processor, when performing the step of determining the similarity between the first data object and the second data object according to the word frequencies corresponding to the first data object and the second data object, comprises:
respectively constructing word frequency vectors corresponding to the first data object and the second data object;
and calculating cosine values between the word frequency vectors corresponding to the first data object and the second data object, and taking the cosine values as the similarity between the first data object and the second data object.
24. The data object recommendation device of claim 16, wherein the processor, in implementing step recommending the second data object, comprises:
adding the second data object to the object list.
25. The data object recommendation device of claim 16, wherein the processor, in implementing step recommending the second data object, comprises:
and setting the second data object as a selectable object and displaying the information of the second data object.
26. The data object recommendation device of claim 16, wherein the processor, in implementing step recommending the second data object, comprises:
determining change information of a transaction cost of the second data object relative to the first data object;
and displaying the information of the second data object and the change information of the transaction cost.
27. The data object recommendation device of claim 26, wherein the change information is arranged to be expressed in an emotionalized manner.
28. The data object recommendation device of claim 17, wherein the processor, in implementing step recommending the second data object, comprises:
in the case that the first data object includes a plurality of data objects and the second data source includes a second data object that is the same as and similar to the data object information of the plurality of first data objects, the second data object that is the same as the data object information of the first data object is presented using the first area, and the second data object that is similar to the data object information of the first data object is presented using the second area.
29. The data object recommendation device of claim 17, wherein the manner in which the first data object is matched to the second data object further comprises:
a data object that said first data object matches functionally in use with said second data object.
30. The data object recommendation device of claim 29, wherein the processor, in implementing step recommendation of the second data object, comprises:
in case the first data object comprises a plurality of and the second data origin comprises a second data object being identical, similar and functionally matching to the data object information of a plurality of the first data objects, presenting the second data object being identical to the data object information of the first data object with a first area, presenting the second data object being similar to the data object information of the first data object with a second area, presenting the second data object being functionally matching to the first data object with a third area.
31. A non-transitory computer readable storage medium having instructions that, when executed by a processor, enable the processor to perform the data object recommendation method of any of claims 1-15.
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| CN114491259A (en) * | 2022-01-26 | 2022-05-13 | 深圳易思智科技有限公司 | Data recommendation method and device, terminal and storage medium |
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