Enhancing conversational recommender systems via multi-level knowledge modeling with semantic relations

Augmenting conversational recommendation systems (CRS) with prior knowledge is crucial for learning user preferences and understanding contextual semantics. Existing methods are mainly based on knowledge graphs (KG), which rely on relationship information between entities, ignoring the semantic relationship in the dialogue. In addition, due to the limited information reflected by structured knowledge in KG, they still need broader external knowledge support to understand contextual semantics. To address the above issues, we propose a knowledge prompt-based CRS named MKCRS, which utilizes semantic relations to model multi-level knowledge and integrate it into CRS sub-tasks. Specifically, we first derive multi-level knowledge representations related to the current dialogue from external knowledge and then perform deep explicit and implicit modeling to construct knowledge prompts. To implement this framework, we design a semantic fusion model based on named entity phrases and a unified knowledge prompt generator. The former exploits explicit semantic relations between entities and contexts to enhance knowledge representations, while the latter adaptively learns the most important knowledge prompts via implicit connections between knowledge. Finally, based on the generated knowledge prompts, we construct knowledge-enhanced prompt templates for recommendation and dialogue generation tasks. Experimental results on the two datasets demonstrate that MKCRS outperforms the state-of-the-art models.