U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan, H.-M. Wang, Fan Feng, Yuan-Mi Chen, Ziyu Zhuang, Hengbin Cui, Yongbin Li, Wanxiang Che
Abstract
Conversational recommender systems ( CRS s) 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. Different from it, we use real user needs as a clue to explore the E-commerce conversational recommendation in complex pre-sales dialogues, namely user needs-centric E-commerce conversational recommendation (UNECR).