Toward Knowledge-Enriched Conversational Recommendation Systems

Tong Zhang, Yong Liu, Boyang Li, Peixiang Zhong, Chen Zhang, Hao Wang, Chunyan Miao


Abstract
Conversational Recommendation Systems recommend items through language based interactions with users.In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.
Anthology ID:
2022.nlp4convai-1.17
Volume:
Proceedings of the 4th Workshop on NLP for Conversational AI
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
NLP4ConvAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
212–217
Language:
URL:
https://aclanthology.org/2022.nlp4convai-1.17
DOI:
10.18653/v1/2022.nlp4convai-1.17
Bibkey:
Cite (ACL):
Tong Zhang, Yong Liu, Boyang Li, Peixiang Zhong, Chen Zhang, Hao Wang, and Chunyan Miao. 2022. Toward Knowledge-Enriched Conversational Recommendation Systems. In Proceedings of the 4th Workshop on NLP for Conversational AI, pages 212–217, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Toward Knowledge-Enriched Conversational Recommendation Systems (Zhang et al., NLP4ConvAI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.nlp4convai-1.17.pdf
Video:
 https://aclanthology.org/2022.nlp4convai-1.17.mp4
Data
ConceptNetReDial