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.

基于个性化商品推荐系统，引入 Co-Clustering Wrapper (CCW) 来提取用户偏好和购买模式，通过计算用户和商品的共同聚类以及为每个聚类添加协同过滤网络来提取内部偏好，结合网络特征获得关于用户的丰富和统一信息。实验结果考虑了根据内部偏好划分的群组数量以及性能改进的量。

揭示和利用图形协作过滤中的团体优先权

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

This paper considers the task of learning users' preferences on a
combinatorial set of alternatives, as generally used by online configurators,
for example. In many settings, only a set of selected alternatives during past
interactions is available to the learner. Fargier et al. [2018] propose an
approach to learn, in such a setting, a model of the users' preferences that
ranks previously chosen alternatives as high as possible; and an algorithm to
learn, in this setting, a particular model of preferences: lexicographic
preferences trees (LP-trees). In this paper, we study complexity-theoretical
problems related to this approach. We give an upper bound on the sample
complexity of learning an LP-tree, which is logarithmic in the number of
attributes. We also prove that computing the LP tree that minimises the
empirical risk can be done in polynomial time when restricted to the class of
linear LP-trees.

该论文研究了基于过去互动中选择的备选方案，来学习用户偏好的任务，提出了一种基于 lexicographic preferences trees (LP-trees) 的偏好模型及学习算法，并研究了与该算法相关的复杂性问题，包括 LP-tree 的学习样本复杂度和在线性 LP-trees 类中计算最小经验风险的算法复杂度等。