UC San Diego

Product reviews are a form of user feedback that provide richer information than traditional signals in recommender systems, such as star ratings and implicit feedback.Meanwhile, a review is also a justification for the user's rating of a product. Previous works show that recommendation models can predict user ratings more accurately by jointly learning to generate reviews. The generated reviews can also serve as recommendation explanations, making the recommender system more interpretable. However, existing works evaluate these generated explanations using traditional natural language generation metrics only, overlooking trustworthiness, an important aspect of model explanations. In this thesis, we focus on two properties of trustworthy recommendation explanations: faithfulness, how truthfully do explanations reflect the decision process of the model, and factuality, whether the generated content accurately reflects the characteristics of the corresponding product. Specifically, this thesis includes two directions: (1) we propose a set of methods for evaluating the faithfulness and semantic coherency of recommendation explanations, and (2) we develop a personalized retrieval-augmented model that can generate factual and informative reviews to explain its recommendation predictions.