ESPRESSO: An Effective Approach to Passage Retrieval for High-Quality Conversational Recommender Systems

Abstract

Conversational Recommender Systems (CRS) aim to provide tailored recommendation responses via a chat interface, including both the user's preferred item and its accompanying explanation. However, due to its generative nature, CRS are prone to responding with factually incorrect explanations (i.e., hallucinations). To solve this problem, we propose incorporating a passage retrieval module into CRS with the objective of enhancing the factuality and informativeness of system responses. Specifically, we outline essential directions for employing a passage retrieval module in CRS to address the following critical issues: (1) the risk of passage retrieval not aligning with the user preference; (2) the absence of supervision for training a passage retrieval module. As a solution, we introduce ESPRESSO, a novel passage retrieval approach for CRS, to effectively tackle the above issues with two core ideas: adaptive item selection and relevance-based groupwise learning. Our extensive experiments show that ESPRESSO effectively resolves issues, achieving up to 36% higher Hit@3 accuracy than the best of 8 competing methods. Additionally, we verify that leveraging passages retrieved by ESPRESSO significantly improves the response quality of CRS.