Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
Aug 13, 20204 views
Conversational recommender systems (CRS) aim to recommend,high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues,still remain to be solved. First, the conversation data itself lacks,of sufficient contextual information for accurately understanding,users’ preference. Second, there is a semantic gap between natural,language expression and item-level user preference.,To address these issues, we incorporate both word-oriented and,entity-oriented knowledge graphs (KG) to enhance the data representations in CRSs, and adopt Mutual Information Maximization,to align the word-level and entity-level semantic spaces. Based on,the aligned semantic representations, we further develop a KGenhanced recommender component for making accurate recommendations, and a KG-enhanced dialog component that can generate informative keywords or entities in the response text. Extensive,experiments have demonstrated the effectiveness of our approach,in yielding better performance on both recommendation and conversation tasks.