This repository contains recent research papers, datasets, and source codes (if any) for Group Recommendation

Code for GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation (SIGIR 2020)

Better youtube recommendations

A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.

Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system

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.

Implementation of the attentive score aggregation models presented in ...

Two Group Recommendation Approaches based on the Contribution of the Users and Pairwise Preferences

A hybrid group recommendation system for film and TV content using Letterboxd profile data

Constructive Preference Elicitation for Social Choice With Setwise max-margin Learning.

Group Recommendation Systems with Diversity-based Clustering and Game Theory

dl-cf-groups-deep-aggregation

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.

This repository will contain Python scripts implementing soft fair aggregation and soft group formation methods for group recommendation.

A group recommendation system

A series of recommender systems projects for DATA.ML.360-2024-2025-1 Recommender Systems at Tampere University

This repository contains the implementation of the paper Modeling Tourist Preference Diversity. The code reproduces the proposed methodology, experiments, and analysis using publicly available datasets. It is intended for research and academic use, supporting transparency and reproducibility of the results.

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.