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，用户对物品进行评分，随后系统基于这些评分为其提供其他产品推荐。尽管用户通常仅对可用物品中的极小部分进行评分，系统仍试图通过发现用户之间及物品之间的相似性来估计未观测到的偏好。本研究将观测到的评分数据视为部分观测、稠密、加权的双部网络。针对一类无外部信息的系统，我们采用一种为稠密加权网络开发的方法进行改进，以处理未观测边及问题的双部特性。该方法允许社区结构存在，并能对不同社区对之间灵活的评分模式进行局部估计。我们在模拟数据集及笑话评分数据集上，将所提方法与现有方法进行性能比较，并在不同稀疏度水平下检验两种情况下模型的性能表现。