Broadening the view: Demonstration-augmented prompt learning for conversational recommendation

• Main Authors: DAO, Quang Huy, DENG, Yang, LE, Dung D., LIAO, Lizi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: conversational recommendation; demonstration-based learning; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9101; https://ink.library.smu.edu.sg/context/sis_research/article/10104/viewcontent/3626772.3657755.pdf
• Institution: Singapore Management University

Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models?To tackle these challenges, we introduce a novel Demonstration-enhanced Conversational Recommender System (DCRS), which aims to strengthen its understanding on the given dialogue contexts by retrieving and learning from demonstrations. In particular, we first propose a knowledge-aware contrastive learning method that adeptly taps into the mentioned entities and the dialogue's contextual essence for pretraining the demonstration retriever. Subsequently, we further develop two adaptive demonstration-augmented prompt learning approaches, involving contextualized prompt learning and knowledge-enriched prompt learning, to bridge the gap between the retrieved demonstrations and the two end tasks of CRS, i.e., response generation and item recommendation, respectively. Rigorous evaluations on two established benchmark datasets underscore DCRS's superior performance over existing CRS methods in both item recommendation and response generation.