Towards Conversational Recommendation over Multi-Type Dialogs

ACL 2020 Zeming LiuHaifeng WangZheng-Yu NiuHua WuWanxiang CheTing Liu

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 user's 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)... (read more)

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