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View all- Wu LZheng ZQiu ZWang HGu HShen TQin CZhu CZhu HLiu QXiong HChen E(2024)A survey on large language models for recommendationWorld Wide Web10.1007/s11280-024-01291-227:5Online publication date: 22-Aug-2024
U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
Authors: 
Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan, Hang Wang, Fan Feng, Yifan Chen, Ziyu Zhuang, Hengbin Cui, Yongbin Li, Wanxiang CheAuthors Info & Claims
Yuanxing Liu, Weinan Zhang, Baohua Dong, Yan Fan, Hang Wang, Fan Feng, Yifan Chen, Ziyu Zhuang, Hengbin Cui, Yongbin Li, Wanxiang CheAuthors Info & ClaimsPages 2723 - 2732
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).
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 on 3 typical categories. Experimental results indicate that the challenges of UNECR in various categories are different.
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Index Terms
- U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation
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Published In
July 2023
3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
- General Chairs:
- Hsin-Hsi Chen,
- Wei-Jou (Edward) Duh,
- Hen-Hsen Huang,
- Program Chairs:
- Makoto P. Kato,
- Josiane Mothe,
- Barbara Poblete
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Published: 18 July 2023
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- Science and Technology Innovation 2030 Major Project of China
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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 23 - 27, 2023
Taipei, Taiwan
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View all- Wu LZheng ZQiu ZWang HGu HShen TQin CZhu CZhu HLiu QXiong HChen E(2024)A survey on large language models for recommendationWorld Wide Web10.1007/s11280-024-01291-227:5Online publication date: 22-Aug-2024
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