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

• URL: http://arxiv.org/abs/2404.17598v1
• Date: Tue, 23 Apr 2024 06:43:58 GMT
• Title: Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
• Authors: Hoin Jung, Hyunsoo Cho, Myungje Choi, Joowon Lee, Jung Ho Park, Myungjoo Kang,
• Abstract summary: We compute co-clusters of users and items with co-clustering algorithms and add CFworks for each cluster to extract the in-group favoritism. 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.
• Score: 9.067610121749777
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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