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.

翻译：在个性化物品推荐系统中，提取用户的偏好与购买模式至关重要。基于现实世界中用户会形成群体且每个群体存在共同偏好的假设，本文提出了协同聚类封装器（Co-Clustering Wrapper, CCW）。我们通过协同聚类算法计算用户与物品的共簇，并为每个簇添加协同过滤子网络以提取群体内偏好。通过整合各网络的特征，我们获得了丰富且统一的用户信息。我们从两个维度对真实世界数据集进行了实验：一是根据群体内偏好划分的群体数量确定，二是衡量性能提升的程度。