E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service

Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He


Abstract
There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. Compared to the traditional recommendation, it advocates wealthier interactions and provides possibilities to obtain users’ exact preferences explicitly. Nevertheless, the corresponding research on this topic is limited due to the lack of broad-coverage dialogue corpus, especially real-world dialogue corpus. To handle this issue and facilitate our exploration, we construct E-ConvRec, an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base (KB), and multiple sequential real conversations between users and recommenders. Next, we explore conversational recommendation in a real scene from multiple facets based on the dataset. Therefore, we particularly design three tasks: user preference recognition, dialogue management, and personalized recommendation. In the light of the three tasks, we establish baseline results on E-ConvRec to facilitate future studies.
Anthology ID:
2022.lrec-1.622
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5787–5796
Language:
URL:
https://aclanthology.org/2022.lrec-1.622
DOI:
Bibkey:
Cite (ACL):
Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, and Xiaodong He. 2022. E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5787–5796, Marseille, France. European Language Resources Association.
Cite (Informal):
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (Jia et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.622.pdf
Data
Coached Conversational Preference ElicitationCookieDuRecDialInspiredOpenDialKGReDialTG-ReDial