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Conversational recommender systems (CRSs) aim to understand the informationneeds and preferences expressed in a dialogue to recommend suitable items tothe user. Most of the existing conversational recommendation datasets aresynthesized or simulated with crowdsourcing, which has a large gap withreal-world scenarios. To bridge the gap, previous work contributes a datasetE-ConvRec, based on pre-sales dialogues between users and customer servicestaff in E-commerce scenarios. However, E-ConvRec only supplies coarse-grainedannotations and general tasks for making recommendations in pre-salesdialogues. Different from that, we use real user needs as a clue to explore theE-commerce conversational recommendation in complex pre-sales dialogues, namelyuser needs-centric E-commerce conversational recommendation (UNECR).  In this paper, we construct a user needs-centric E-commerce conversationalrecommendation dataset (U-NEED) from real-world E-commerce scenarios. U-NEEDconsists of 3 types of resources: (i) 7,698 fine-grained annotated pre-salesdialogues in 5 top categories (ii) 333,879 user behaviors and (iii) 332,148product knowledge tuples. To facilitate the research of UNECR, we propose 5critical tasks: (i) pre-sales dialogue understanding (ii) user needselicitation (iii) user needs-based recommendation (iv) pre-sales dialoguegeneration and (v) pre-sales dialogue evaluation. We establish baseline methodsand evaluation metrics for each task. We report experimental results of 5 taskson U-NEED. We also report results in 3 typical categories. Experimental resultsindicate that the challenges of UNECR in various categories are different.

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