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Unveiling User Preferences: A Knowledge Graph and LLM-Driven Approach for Conversational Recommendation
Zhangchi Qiu, Linhao Luo, Shirui Pan, Alan Wee-Chung Liew
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations through dynamically capturing user preferences in interactive conversations. Conventional CRSs often extract user preferences as hidden representations, which are criticized for their lack of interpretability. This diminishes the transparency and trustworthiness of the recommendation process. Recent works have explored combining the impressive capabilities of Large Language Models (LLMs) with the domain-specific knowledge of Knowledge Graphs (KGs) to generate human-understandable recommendation explanations. Despite these efforts, the integration of LLMs and KGs for CRSs remains challenging due to the modality gap between unstructured dialogues and structured KGs. Moreover, LLMs pre-trained on large-scale corpora may not be well-suited for analyzing user preferences, which require domain-specific knowledge. In this paper, we propose COMPASS, a plug-and-play framework that synergizes LLMs and KGs to unveil user preferences, enhancing the performance and explainability of existing CRSs. To address integration challenges, COMPASS employs a two-stage training approach: first, it bridges the gap between the structured KG and natural language through an innovative graph entity captioning pre-training mechanism. This enables the LLM to transform KG entities into concise natural language descriptions, allowing them to comprehend domain-specific knowledge. Following, COMPASS optimizes user preference modeling via knowledge-aware instruction fine-tuning, where the LLM learns to reason and summarize user preferences from both dialogue histories and KG-augmented context. This enables COMPASS to perform knowledge-aware reasoning and generate comprehensive and interpretable user preferences that can seamlessly integrate with existing CRS models for improving recommendation performance and explainability.
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