Multi-aspect Knowledge-enhanced Hypergraph Attention Network for Conversational Recommendation Systems

Conversational recommendation systems (CRS) aim to proactively elicit user preferences through multi- turn conversations for item recommendations. However, most existing works focus solely on user's current conversation information, which fails to capture user implicit preferences comprehensively. Moreover, these approaches primarily center around pairwise relations among data in CRS to enhance item representations, while largely overlooking the complicated relationships in CRS. To address these limitations, we propose a hypergraph-based knowledge-enhanced CRS model namely Multi-aspect Knowledge-enhanced Hypergraph Attention Network for Conversational Recommendation Systems ( MKHCR ). We construct three hypergraphs based on multiple aspects knowledge to mine high-order relations among data for enhancing user implicit preference representations. Specifically, we build a session hypergraph to capture high-order complicated relations in the historical conversations to explore user implicit preferences. To mitigate the data scarcity issue, we incorporate knowledge graphs and items review information, modeling them within hypergraph structure to learn complicated semantic relationships, thereby enhancing item representations. Moreover, a hypergraph attention network with a dual attention mechanism is proposed to flexibly aggregate important high-order features from these hypergraphs, which contributes to enhance user preference representations for both the recommendation and conversation generation tasks. Extensive experiments on two publicly available CRS datasets validate the effectiveness of our proposed MKHCR model, which exhibits significant improvements across key evaluation metrics, including HR@50, MRR@50, and NDCG@50, achieving enhancements of 6.76%, 9.16%, and 7.92%, respectively.