Recommendation Method Integrating Review Text Hierarchical Attention with Time Information

Abstract

The data sparsity problem caused by information overload restricts the recommendation performance of the matrix factorization model based on scoring data. The recommendation model integrated with reviews text can effectively alleviate the sparsity of scoring data. The current recommendation system uses reviews text to model users and items, most of them use users' reviews text on items as the data sources, and ignore the impacts of time information on user and item attributes. In response to this problem, a recommendation method which integrates review text hierarchical attention with time information (RHATR) is proposed. This method can fully mine the potential semantic information of review text and model the dynamic changes of user perferences and item features. This method mines effective information such as sentiment words and keywords in a single review text, learns user and item representation by applying word level attention to a single review text, applies review level attention to extracting effective reviews respectively for the user review set and item review set with temporal factor and further learns the dynamic representation of user preferences and item features. Finally, the user and item representation learned from the review text, and ID based item and user embedding as the final features, it captures the potential factors of each user and item. The experimental results show that the proposed method has a better effect in root mean square error (RMSE) on Amazon and Yelp datasets than the current baseline method.

Keywords

• recommendation system
• review text
• hierarchical attention
• time information