Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
This work addresses recommendation systems for users by proposing a method to leverage in-group favoritism, but it appears incremental as it builds on existing co-clustering and collaborative filtering techniques.
The paper tackled the problem of personalized item recommendation by assuming users form clusters with common favoritism, introducing Co-Clustering Wrapper (CCW) to extract in-group favoritism, resulting in improved performance measured on real-world datasets.
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.