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

Yuanxing Liu, Weinan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, Wanxiang Che

Abstract
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 E-commerce pre-sales dialogues. We analyze the impact of two collaborative approaches with two CRSs and two LLMs on four tasks of E-commerce pre-sales dialogue. We find that collaborations between CRS and LLM can be very effective in some cases.
Anthology ID:
2023.findings-emnlp.643
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9587–9605
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.643
DOI:
10.18653/v1/2023.findings-emnlp.643
Bibkey:
Cite (ACL):
Yuanxing Liu, Weinan Zhang, Yifan Chen, Yuchi Zhang, Haopeng Bai, Fan Feng, Hengbin Cui, Yongbin Li, and Wanxiang Che. 2023. Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9587–9605, Singapore. Association for Computational Linguistics.
Cite (Informal):
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue (Liu et al., Findings 2023)
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