In recommender systems, users rate items, and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. In this work, we treat the observed ratings data as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, we adapt an approach developed for dense, weighted networks to account for unobserved edges and the bipartite nature of the problem. This approach allows for community structure, and for local estimation of flexible patterns of ratings across different pairs of communities. We compare the performance of our proposed approach to existing methods on a simulated data set, as well as on a data set of joke ratings, examining model performance in both cases at differing levels of sparsity.

翻译：在推荐系统中，用户对项目进行评分，系统根据这些评分向用户推荐其他产品。尽管用户通常仅对可用项目中的极小部分进行评分，系统仍试图通过挖掘用户间及项目间的相似性来估计未观测到的偏好。本文将观测到的评分数据视为部分可观测的、密集的、加权的二分网络。针对一类缺乏外部信息的系统，我们改进了一种适用于密集加权网络的方法，以处理未观测边以及问题的二分网络特性。该方法既能刻画社区结构，又能对不同社区对间的评分模式进行局部灵活估计。我们通过模拟数据集及笑话评分数据集，将所提方法的表现与现有方法进行对比，并在不同稀疏度水平下检验模型的性能。