# U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation arXiv: https://arxiv.org/abs/2305.04774 ยท HTML: https://mlsanity.com/paper/2305.04774/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 58 references that are in the map. - First posted on arXiv 5 May 2023. ## Builds on (most cited first, 25 of 25) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018) - [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018) - [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020) - [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020) - [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021) - [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019) - [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020) - [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020) - [INSPIRED: Toward Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2009.14306/) (2020) - [RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems](https://mlsanity.com/paper/1701.03079/) (2017) - [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019) - [Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining](https://mlsanity.com/paper/2009.11321/) (2020) - [Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings](https://mlsanity.com/paper/1904.10635/) (2019) - [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021) - [DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation](https://mlsanity.com/paper/2109.08877/) (2021) - [Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue](https://mlsanity.com/paper/1909.03922/) (2019) - [Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems](https://mlsanity.com/paper/1511.06931/) (2015) - [CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://mlsanity.com/paper/2101.00939/) (2021) - [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://mlsanity.com/paper/2201.02732/) (2022) - [Learning Neural Templates for Recommender Dialogue System](https://mlsanity.com/paper/2109.12302/) (2021) - [User Memory Reasoning for Conversational Recommendation](https://mlsanity.com/paper/2006.00184/) (2020) - [Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.09781/) (2020) - [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022) ## Built on by (most cited first, 12 of 12) - [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023) - [ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents](https://mlsanity.com/paper/2508.04266/) (2025) - [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023) - [LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation](https://mlsanity.com/paper/2503.23312/) (2025) - [EComStage: Stage-wise and Orientation-specific Benchmarking for Large Language Models in E-commerce](https://mlsanity.com/paper/2601.02752/) (2026) - [ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation](https://mlsanity.com/paper/2602.23716/) (2026) - [MobileConvRec: A Conversational Dataset for Mobile Apps Recommendations](https://mlsanity.com/paper/2405.17740/) (2024) - [A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions](https://mlsanity.com/paper/2506.17285/) (2025) - [HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation](https://mlsanity.com/paper/2607.17461/) (2026) - [Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation](https://mlsanity.com/paper/2607.18609/) (2026) - [Match Chat: Real Time Generative AI and Generative Computing for Tennis](https://mlsanity.com/paper/2509.12592/) (2025) - [FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System](https://mlsanity.com/paper/2609.20175/) (2026) ## Abstract (arXiv) 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.