Aligning Recommendation and Conversation via Dual Imitation
Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou
Abstract
Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.- Anthology ID:
- 2022.emnlp-main.36
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 549–561
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.36
- DOI:
- 10.18653/v1/2022.emnlp-main.36
- Cite (ACL):
- Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. 2022. Aligning Recommendation and Conversation via Dual Imitation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 549–561, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Aligning Recommendation and Conversation via Dual Imitation (Zhou et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.36.pdf