  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.
