Accurate and Explainable Recommendation via Review Rationalization
Sicheng Pan, Dongsheng Li, Hansu Gu, Tun Lu, Xufang Luo, Ning Gu
Abstract
Auxiliary information, such as reviews, have been widely adopted to improve collaborative filtering (CF) algorithms, e.g., to boost the accuracy and provide explanations. However, most of the existing methods cannot distinguish between co-appearance and causality when learning from the reviews, so that they may rely on spurious correlations rather than causal relations in the recommendation — leading to poor generalization performance and unconvincing explanations. In this paper, we propose a Recommendation via Review Rationalization (R3) method including 1) a rationale generator to extract rationales from reviews to alleviate the effects of spurious correlations; 2) a rationale predictor to predict user ratings on items only from generated rationales; and 3) a correlation predictor upon both rationales and correlational features to ensure conditional independence between spurious correlations and rating predictions given causal rationales. Extensive experiments on real-world datasets show that the proposed method can achieve better generalization performance than state-of-the-art CF methods and provide causal-aware explanations even when the test data distribution changes.