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

arXiv:2404.17598v1 Announce Type: cross
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 …

abstract arxiv cluster clustering collaborative collaborative filtering cs.ai cs.ir cs.lg extract filtering form graph graph-based patterns personalized recommendation type work world wrapper