Towards Conversational Recommendation over Multi-Type Dialogs
release_h74ayat5k5cxrhz6q2fhtazvie
by
Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu
2020
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. Dataset and codes are publicly
available at
https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.
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