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    <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.</abstract>
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%0 Conference Proceedings
%T Towards Conversational Recommendation over Multi-Type Dialogs
%A Liu, Zeming
%A Wang, Haifeng
%A Niu, Zheng-Yu
%A Wu, Hua
%A Che, Wanxiang
%A Liu, Ting
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-towards-conversational
%X 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.
%R 10.18653/v1/2020.acl-main.98
%U https://aclanthology.org/2020.acl-main.98/
%U https://doi.org/10.18653/v1/2020.acl-main.98
%P 1036-1049

Markdown (Informal)

[Towards Conversational Recommendation over Multi-Type Dialogs](https://aclanthology.org/2020.acl-main.98/) (Liu et al., ACL 2020)

• Towards Conversational Recommendation over Multi-Type Dialogs (Liu et al., ACL 2020)

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.