Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
This work addresses a key bottleneck in conversational recommender systems for improving recommendation accuracy, though it is incremental as it builds on prior context-aware methods.
The paper tackles the challenge of combining different types of contextual information in conversational recommender systems by proposing MCCRS, which fuses structured and unstructured data via a mixture-of-experts approach, achieving significantly higher performance than existing baselines.
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.