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Explicit Knowledge Graph Reasoning for Conversational Recommendation by Ren, Xuhui; Chen, Tong; Nguyen, Quoc Viet Hung; Cui, Lizhen; Huang, Zi; Yin, Hongzhi is a scholarly article available to read on EtoBox.

What is Explicit Knowledge Graph Reasoning for Conversational Recommendation about?

Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests and letting users receive recommendation only passively. Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions. Despite the progress achieved in CRSs, existing solutions are far from satisfaction in the following two aspects: 1) current CRSs usually require each user to answer a quantity of clarifying questions before reaching the final recommendation, which harms the user experience; 2) there is a semantic gap between the learned representations of explicitly mentioned attributes and items. To address these drawbacks, we introduce the knowledge graph (KG) as the auxiliary information for comprehending and reasoning a user's preference, and propose a new CRS framework, namely Knowledge Enhanced Conversational Reasoning (KECR) system. As a user can reflect her/his preference via both attribute- and item-level expressions, KECR closes the semantic gap be

Author

Ren, Xuhui; Chen, Tong; Nguyen, Quoc Viet Hung; Cui, Lizhen; Huang, Zi; Yin, Hongzhi

Published

2023

Language

EN