Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation

The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors.