KECRS: Towards Knowledge-Enriched Conversational Recommendation System

• URL: http://arxiv.org/abs/2105.08261v1
• Date: Tue, 18 May 2021 03:52:06 GMT
• Title: KECRS: Towards Knowledge-Enriched Conversational Recommendation System
• Authors: Tong Zhang, Yong Liu, Peixiang Zhong, Chen Zhang, Hao Wang, Chunyan Miao
• Abstract summary: chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. external knowledge graphs (KG) have been introduced into chit-chat-based CRS. We propose the Knowledge-Enriched Conversational Recommendation System (KECRS) Experimental results on a large-scale dataset demonstrate that KECRS outperforms state-of-the-art chit-chat-based CRS.
• Score: 50.0292306485452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions. To better understand user's intentions, external knowledge graphs (KG) have been introduced into chit-chat-based CRS. However, existing chit-chat-based CRS usually generate repetitive item recommendations, and they cannot properly infuse knowledge from KG into CRS to generate informative responses. To remedy these issues, we first reformulate the conversational recommendation task to highlight that the recommended items should be new and possibly interested by users. Then, we propose the Knowledge-Enriched Conversational Recommendation System (KECRS). Specifically, we develop the Bag-of-Entity (BOE) loss and the infusion loss to better integrate KG with CRS for generating more diverse and informative responses. BOE loss provides an additional supervision signal to guide CRS to learn from both human-written utterances and KG. Infusion loss bridges the gap between the word embeddings and entity embeddings by minimizing distances of the same words in these two embeddings. Moreover, we facilitate our study by constructing a high-quality KG, \ie The Movie Domain Knowledge Graph (TMDKG). Experimental results on a large-scale dataset demonstrate that KECRS outperforms state-of-the-art chit-chat-based CRS, in terms of both recommendation accuracy and response generation quality.

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