A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable Recommendation

Abstract

Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment information or review information, does not explicitly incorporate review-level sentiment information into the model, is based on review retrieval, and generates explanations in the form of templates or phrases. To tackle the above limitations, this article proposes a REview-level Sentiment information enhanced multiTask learning approach for Explainable Recommendation (RESTER). Specifically, it first considers the user's review information and analyzes the sentiment polarity contained in the review. Then, the user/item's identity feature, review feature, and sentiment information are fused into a multitask learning framework by leveraging the implicit correlation between the rating prediction and explanation generation tasks. Comprehensive experiments on datasets in three different domains have shown that the proposed model is superior to all other baselines in both rating prediction and explanation generation tasks.