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Improving conversational recommender systems via multi-preference and-enhanced
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AI
DOI：10.1016/j.knosys.2023.111361.png)
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Abstract
En 中文
Conversational recommender systems (CRS) aim to capture user preferences and provide high -quality recommendations through conversations. Existing CRSs use items in conversations to represent user preferences, and these items usually need to match corresponding entities in external knowledge graphs (KGs). However, current general approaches extract subgraphs from large KGs (e.g., DBpedia) and thus may contain noise from nonrelevant domains and do not cover items in the CRS domain well. In addition, existing CRSs do not focus on hidden semantic preferences in user conversations, nor do they emphasize item attributes. Moreover, the system -generated responses lack diversity and descriptiveness. To address these issues, we propose a novel approach for Improving Conversation Recommendation Systems via Multi -Preference Modelling and Knowledge -Enhanced (MPKE). Specifically, we first customize a higher -quality knowledge graph LMKG for CRS to help the model fully exploit external knowledge to improve its performance. Second, we design a semantic fusion module to augment the semantic connections between external knowledge and dialogue text. Then, we propose a two -stage recommendation mechanism to explore more item attributes and fuse entity and text semantics to enhance user representation. For response generation, we use a word -level KG (i.e., ConceptNet) to enhance the representation of conversation keywords and construct explanation templates with slots to enhance the diversity of system responses. extensive experiments show that our approach outperforms past state-of-the-art methods. the source code will be published at https://github.com/zcy-cqut/MPKE.
Keywords:
Conversational recommender system
Knowledge graph
Multi-preference modelling
Pre-trained language model
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K
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7.6
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