Auxiliary information, e.g., reviews, is widely adopted to improve collaborative filtering (CF) algorithms, e.g., to boost accuracy and provide explanations. However, most of the existing methods cannot distinguish between co-appearance and causality when learning from reviews, so that they may rely on spurious correlations rather than causal relations in 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 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
Auxiliary information, e.g., reviews, is widely adopted to improve collaborative filtering (CF) algorithms, e.g., to boost accuracy and provide explanations. However, most of the existing methods cannot distinguish between co-appearance and causality when learning from reviews, so that they may rely on spurious correlations rather than causal relations in recommendation — leading to poor generalization 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