Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
<|reference_start|>Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering: 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.<|reference_end|>
@article{jung2024revealing,
    title={Revealing and Utilizing In-group Favoritism for Graph-based
  Collaborative Filtering},
    author={Hoin Jung, Hyunsoo Cho, Myungje Choi, Joowon Lee, Jung Ho Park,
  Myungjoo Kang},
    journal={arXiv preprint arXiv:2404.17598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17598},
    primaryClass={cs.IR cs.AI cs.LG cs.SI}
}