论文标题
社会推荐系统的序数图伽马信念网络
Ordinal Graph Gamma Belief Network for Social Recommender Systems
论文作者
论文摘要
为了构建建议的系统，不仅考虑用用户 - 项目交互为序数变量，还利用了描述用户之间关系的社交网络，我们开发了一个层次的贝叶斯模型称为序数图因子分析（OGFA），该模型共同对用户和用户 - 用户 - 用户 - 用户交互建模。 OGFA不仅可以实现良好的建议性能，而且还提取与代表性用户偏好相对应的可解释潜在因素。我们进一步将OGFA扩展到Oldinal Graph Gamma信念网络，该网络是一个多策略层的深层概率模型，可在多个语义级别捕获用户偏好和社交社区。为了高效的推断，我们开发了一种并行的混合吉布斯 - EM算法，该算法利用了图的稀疏性，并且可扩展到大型数据集。我们的实验结果表明，所提出的模型不仅在具有明确或隐式反馈的推荐数据集上优于最新基准，而且还提供了可解释的潜在表示。
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.
