Towards Conversational Recommendation over Multi-Type Dialogs

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 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. Finally we establish baseline results on DuRecDial for future studies.1

Introduction. In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots (Christakopoulou et al., 2016; Li et al., 2018; Reschke et al., 2013; Warnestal, 2005). They focus on how to provide high-quality recommendations through dialog-based interactions with users. These work fall into two categories: (1) task-oriented dialogmodeling approaches (Christakopoulou et al., 2016; Sun and Zhang, 2018; Warnestal, 2005); (2) nontask dialog-modeling approaches with more freeform interactions (Kang et al., 2019; Li et al., 2018). Almost all these work focus on a single type of dialogs, either task oriented dialogs for recommendation, or recommendation oriented open-domain conversation. Moreover, they assume that both sides in the dialog (especially the user) are aware of the conversational goal from the beginning.

Discussion / Conclusion. We identify the task of conversational recommendation over multi-type dialogs, and create a dataset DuRecDial with multiple dialog types and multidomain use cases. We demonstrate usability of this dataset and provide results of state of the art models for future studies. The complexity in DuRecDial makes it a great testbed for more tasks such as knowledge grounded conversation (Ghazvininejad et al., 2018), domain transfer for dialog modeling, target-guided conversation (Tang et al., 2019a) and multi-type dialog modeling (Yu et al., 2017). The study of these tasks will be left as the future work.