A hierarchical attention model for rating prediction by leveraging user and product reviews

Abstract

Recently, recommendations accompanied by review text have become increasingly common. Some studies have incorporated user and product information to generate review representations; however, the corresponding methods generally suffer from high model complexity and only consider textual review information at the word level rather than the semantic level. To address the above issues, we present a hierarchical attention model called HAUP based on user and product reviews. The model jointly learns user and product information from ratings and user review text in recommendation. First, a hierarchical bidirectional gated recurrent unit (Bi-GRU) is constructed that includes word and sentence-level review information. The Bi-GRU structure can handle long-range dependency in the review text. Then, attention mechanisms are applied at the word and sentence levels to identify the most important content when constructing the review document representation. Finally, the generated latent user and product representations are merged into the same vector space for estimating ratings. Experiments conducted based on two real-world data sets demonstrate that the proposed architecture outperforms existing state-of-the-art models. A visualization of the different attention layers demonstrates that the proposed model selects important words and sentences in review documents.