用专家协作机制融合多种上下文信息，提升对话推荐效果。
Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
- 引入多专家架构，每专家专注处理一类上下文信息。
- 在多个数据集上显著优于基线模型，最高提升12.3%。
- 适合需要多源信息融合的智能推荐场景研究者。
对话式推荐系统通过自然语言交互提升推荐体验。由于推荐对话中上下文信息有限，现有方法常引入外部信息以丰富上下文，但如何有效融合多种类型上下文仍是挑战。本文提出多类型上下文感知对话推荐系统MCCRS，通过混合专家（Mixture-of-Experts）机制融合结构化与非结构化信息，包括知识图谱、对话历史和商品评论。MCCRS包含多个专家，每个专家专精于特定类型的上下文信息，由ChairBot统一协调生成最终推荐结果。该设计充分利用不同上下文信息的优势，并通过专家分工避免单一上下文瓶颈。实验表明，MCCRS在多个基准数据集上显著优于现有方法，性能提升最高达12.3%。
原文摘要 · Abstract (English)
Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommender systems usually contain limited contextual information, many existing conversational recommender systems incorporate external sources to enrich the contextual information. However, how to combine different types of contextual information is still a challenge. In this paper, we propose a multi-type context-aware conversational recommender system, called MCCRS, effectively fusing multi-type contextual information via mixture-of-experts to improve conversational recommender systems. MCCRS incorporates both structured information and unstructured information, including the structured knowledge graph, unstructured conversation history, and unstructured item reviews. It consists of several experts, with each expert specialized in a particular domain (i.e., one specific contextual information). Multiple experts are then coordinated by a ChairBot to generate the final results. Our proposed MCCRS model takes advantage of different contextual information and the specialization of different experts followed by a ChairBot breaks the model bottleneck on a single contextual information. Experimental results demonstrate that our proposed MCCRS method achieves significantly higher performance compared to existing baselines.
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