  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. Different from that, 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).
  In this paper, we construct a user needs-centric E-commerce conversational
recommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEED
consists of 3 types of resources: (i) 7,698 fine-grained annotated pre-sales
dialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148
product knowledge tuples. To facilitate the research of UNECR, we propose 5
critical tasks: (i) pre-sales dialogue understanding (ii) user needs
elicitation (iii) user needs-based recommendation (iv) pre-sales dialogue
generation and (v) pre-sales dialogue evaluation. We establish baseline methods
and evaluation metrics for each task. We report experimental results of 5 tasks
on U-NEED. We also report results in 3 typical categories. Experimental results
indicate that the challenges of UNECR in various categories are different.
