Towards Conversational Recommendation over Multi-Type Dialogs

Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu


Abstract
We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user’s interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset DuRecDial (about 10k dialogs, 156k utterances), where there are multiple sequential dialogs for a pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies.
Anthology ID:
2020.acl-main.98
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1036–1049
Language:
URL:
https://aclanthology.org/2020.acl-main.98
DOI:
10.18653/v1/2020.acl-main.98
Bibkey:
Cite (ACL):
Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting Liu. 2020. Towards Conversational Recommendation over Multi-Type Dialogs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1036–1049, Online. Association for Computational Linguistics.
Cite (Informal):
Towards Conversational Recommendation over Multi-Type Dialogs (Liu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.98.pdf
Video:
 http://slideslive.com/38929296
Code
 PaddlePaddle/models +  additional community code
Data
DuRecDialCMU DoGKdConvOpenDialKG