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
- Editors:
- Bing Liu, Alexandros Papangelis, Stefan Ultes, Abhinav Rastogi, Yun-Nung Chen, Georgios Spithourakis, Elnaz Nouri, Weiyan Shi
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.nlp4convai-1.17.pdf
- Data
- ConceptNet, ReDial