Research Area:  Machine Learning

Abstract:

We propose a new task of conversational recommendation over 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 users interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every 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.

Keywords:  
Conversational Recommendation
Multi-Type Dialogs
Computation and Language
Artificial Intelligence

Author(s) Name:  Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu

Publisher name:  arXiv.2005.03954

DOI:  10.48550/arXiv.2005.03954

Volume Information:  

Paper Link:   https://arxiv.org/abs/2005.03954