U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation

Understand

Conversational recommender systems (CRSs) aim to understand the information needs and preferences expressed in a dialogue to recommend suitable items to the user.

• Most of the existing conversational recommendation datasets are synthesized or simulated with crowdsourcing, which has a large gap with real-world scenarios.
• To bridge the gap, previous work contributes a dataset E-ConvRec, based on pre-sales dialogues between users and customer service staff in E-commerce scenarios.
• However, E-ConvRec only supplies coarse-grained annotations and general tasks for making recommendations in pre-sales dialogues.