Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models

Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over graph relationships, indiscriminately incorporating retrieved knowledge without context filtering, and neglecting collaborative preferences in multi-turn dialogues. To this end, we propose PCRS-TKA, a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs. PCRS-TKA constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning while capturing rich entity semantics. Our approach selectively filters context-relevant knowledge and explicitly models collaborative preferences using specialized supervision signals. A semantic alignment module harmonizes heterogeneous inputs, reducing noise and enhancing accuracy. Extensive experiments demonstrate that PCRS-TKA consistently outperforms all baselines in both recommendation and conversational quality.

Authors

• Yongwen Ren School of Computer Science and Technology, University of Science and Technology of China iFLYTEK AI Research, iFLYTEK Co.,Ltd
• Chao Wang Tsinghua University
• Peng Du School of Software and Microelectronics, Peking University
• Chuan Qin
• Dazhong Shen
• Hui Xiong Hong Kong University of Science and Technology (Guangzhou)

Keywords

No keywords are indexed for this paper.