Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or the abstract of a journal article. In this paper, a novel model called Collaborative Poisson Factorization with Side-information (CPFS) is proposed which extends CTPF by incorporating richer kinds of side information conditionally as a prior to the model. CPFS is a monolithic hybridization model that combines features from different data sources into a single recommendation algorithm. We develop a Gibbs sampler and also a Variational method with closed-form updates for the inference of CPFS and demonstrate its applicability on a range of datasets including movies, books, academic papers, and travel. The extension improves prediction quality, especially in the cold start scenario. The connections between side information and topics are also intuitive.

协作过滤方法仅通过使用评分矩阵即可预测用户兴趣并提出建议。但是，实际上，存在有关用户和项目的大量辅助信息，例如用户的年龄，电影中的演员或期刊文章的摘要。在本文中，提出了一种名为“带有边信息的协同泊松因子分解”（CPFS）的新型模型，该模型通过在模型之前先有条件地合并更多种类的边信息来扩展CTPF。CPFS是一种单片混合模型，它将来自不同数据源的特征组合到单个推荐算法中。我们开发了Gibbs采样器以及带有闭式更新的变分方法来推断CPFS，并证明了其在电影，书籍，学术论文和旅行等一系列数据集上的适用性。该扩展提高了预测质量，尤其是在冷启动场景中。辅助信息和主题之间的联系也很直观。