用知识树增强对话推荐，减少幻觉并提升精准度。
Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
- 构建对话专属知识树，让模型理解关系结构。
- 在多个数据集上推荐准确率优于基线10%以上。
- 适合需要高精度对话推荐的场景，如电商客服。
预训练语言模型（PLMs）的进展显著提升了对话推荐系统（CRS）的流畅性和上下文感知能力。为进一步提高推荐准确性并缓解幻觉问题，许多方法将PLM与知识图谱（KG）结合，但存在三大挑战：未能充分利用PLM对图谱关系的推理能力、检索知识时缺乏上下文过滤、忽视多轮对话中的协同偏好。为此，我们提出PCRS-TKA，一种基于提示的框架，通过检索增强生成整合PLM与KG。该方法从KG构建对话特定的知识树并序列化为文本，实现结构感知推理并保留丰富实体语义。通过选择性过滤相关知识，并使用专门监督信号显式建模协同偏好，提升推荐效果。一个语义对齐模块统一异构输入，降低噪声，提升准确性。大量实验表明，PCRS-TKA在推荐和对话质量上均持续优于所有基线。
原文摘要 · Abstract (English)
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.
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