To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We further extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model that captures the user preferences and social communities at multiple semantic levels. For efficient inference, we develop a parallel hybrid Gibbs-EM algorithm, which exploits the sparsity of the graphs and is scalable to large datasets. Our experimental results show that the proposed models not only outperform recent baselines on recommendation datasets with explicit or implicit feedback, but also provide interpretable latent representations.
翻译:为了建立建议系统,不仅将用户-项目互动视为正统变量,而且利用描述用户之间关系的社会网络,我们开发了一种称为正统图形要素分析(OGFA)的等级性贝叶斯模型(OGFA),该模型共同模拟用户-项目和用户-用户互动。OGFA不仅取得了良好的建议性能,还提取了与有代表性用户喜好相对应的可解释的潜在因素。我们进一步将OGFA扩展至正方形伽玛信仰网络,这是一个多层次的多层次深深层次概率模型,在多个语义层面上捕捉用户偏好和社会社群。为了高效的推断,我们开发了一个平行的混合 Gibs-EM 算法,该算法利用了图形的广度,对大数据集来说是可缩放的。我们的实验结果表明,拟议的模型不仅超越了建议数据集最近的基线,有明确或隐含的反馈,而且还提供了可解释的潜在表达方式。