Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering

TL;DR

This paper introduces a co-clustering based approach to enhance graph-based collaborative filtering by revealing and utilizing in-group favoritism among users, leading to improved recommendation performance.

Contribution

It proposes the Co-Clustering Wrapper (CCW) method that integrates user and item co-clusters with collaborative filtering networks to leverage in-group favoritism.

Findings

Improved recommendation accuracy on real-world datasets.

Effective identification of user groups based on in-group preferences.

Enhanced feature representation for personalized recommendations.

Abstract

When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and unified information about users. We experimented real world datasets considering two aspects: Finding the number of groups divided according to in-group preference, and measuring the quantity of improvement of the performance.