1. Paper title

Towards Conversational Recommendation over Multi-Type Dialogs

2. link

https://www.aclweb.org/anthology/2020.acl-main.98.pdf

3. 摘要

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 nonrecommendation 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

[?] conversational recommendation

4. 要解决什么问题

多类型对话中的对话推荐，可以根据用户的兴趣的反馈，主动自动地从非推荐对话引导到推荐对话。

5. 作者的主要贡献

创建了人与人之间的中文对话数据集DuRecDial 在DuRecDial上建立基线结果

6. 得到了什么结果

以备将来之用。

7. 关键字

数据集