Code for GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation (SIGIR 2020)
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Updated
Jul 6, 2023 - Python
Code for GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation (SIGIR 2020)
This repository contains recent research papers, datasets, and source codes (if any) for Group Recommendation
This repository contains a Python script that implements a novel group recommender system based on the research paper titled "A novel group recommender system based on members’ influence and leader impact" by Reza Barzegar Nozari and Hamidreza Koohi.
Two Group Recommendation Approaches based on the Contribution of the Users and Pairwise Preferences
Better youtube recommendations
Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system
This repository will contain Python scripts implementing soft fair aggregation and soft group formation methods for group recommendation.
Project for the lessons on Fairness Recommendation, held by professor Kostas Stefanidis, from the course Advanced Topics in Computer Science (ATCS) at Roma Tre University.
The recommender written during my thesis.
dl-cf-groups-deep-aggregation
A hybrid group recommendation system for film and TV content using Letterboxd profile data
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
Implementation of the attentive score aggregation models presented in ...
Member contribution-based group recommender system
IBGR (Influence-Based Group Recommendation) is a novel group recommendation that published in Knowledge-Based System journal at Elsevier. It takes into account the influence of members and leaders in groups to determine items rating proper to all members for groups. There is a sample MATLAB code of IBGR for a fixed group with 4 member and 7 items.
Constructive Preference Elicitation for Social Choice With Setwise max-margin Learning.
A group recommendation system
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