Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top-$N$ recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.

论文提出了一种统一的框架 ——Preference Network (PN)，用于推荐系统任务。PN 将基于内容过滤和协作过滤相结合，支持评分预测和 top-N 推荐等查询，并采用了一种简单但有效的伪似然估计方法来解决大规模用户和项目网络学习的难题。实验表明 PN 具有明显的优点。