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

Liu, Yuanxing; Zhang, Weinan; Dong, Biao; Fan, Yan; Wang, H.; Feng, Feng; Chen, Yifan; Zhuang, Ziyu; Cui, Hengbin; Li, Yongbin; Che, Wanxiang

doi:10.1145/3539618.3591878

Cited by 2 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We select typical methods for the four tasks following (Liu et al, 2023b). Specifically, we select Bert, Bert+CRF, Bert+BiLSTM+CRF as baselines for pre-sales dialogue.…”

Section: Baseline Methodsmentioning

confidence: 99%

“…In this paper, we explore the effectiveness of collaboration by analyzing the impact of collaboration between CRS and LLM on the performance of four tasks in E-commerce pre-sales dialogue (Liu et al, 2023b). These tasks include: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation and (iv) pre-sales dialogue generation.…”

Section: Overviewmentioning

confidence: 99%

“…We conduct experiments on U-NEED (Liu et al, 2023b). U-NEED consists of 7,698 fine-grained annotated pre-sales dialogues, which consist of 1662, 1513, 1135, 1748, and 1640 dialogues in Beauty, Phones, Fashion, Shoes and Electronics categories respectively.…”

Section: Datasetmentioning

confidence: 99%

“…A high-quality pre-sales dialogue can greatly increase the purchase rate of a user. However, there are many challenges to providing a high-quality pre-sales dialogue service (Liu et al, 2023b). Refine.…”

mentioning

confidence: 99%

“…Specifically, we explore the effectiveness of collaborations on a real-world dataset of E-commerce pre-sales dialogues, namely U-NEED (Liu et al, 2023b). U-NEED contains pre-sales dialogues in five top categories and supports four key tasks in E-commerce pre-sales dialogue: (i) pre-sales dialogue understanding (ii) user needs elicitation (iii) user needs-based recommendation and (iv) pre-sales dialogue generation.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue

Liu,

Zhang,

Chen

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

View full text Add to dashboard Cite

E-commerce pre-sales dialogue aims to understand and elicit user needs and preferences for the items they are seeking so as to provide appropriate recommendations. Conversational recommender systems (CRSs) learn user representation and provide accurate recommendations based on dialogue context, but rely on external knowledge. Large language models (LLMs) generate responses that mimic pre-sales dialogues after fine-tuning, but lack domain-specific knowledge for accurate recommendations. Intuitively, the strengths of LLM and CRS in E-commerce pre-sales dialogues are complementary, yet no previous work has explored this. This paper investigates the effectiveness of combining LLM and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods: CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of two collaborative approaches with two CRSs and two LLMs on four tasks of Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM can be very effective in some cases.

show abstract

“…We select typical methods for the four tasks following (Liu et al, 2023b). Specifically, we select Bert, Bert+CRF, Bert+BiLSTM+CRF as baselines for pre-sales dialogue.…”

Section: Baseline Methodsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations