Peiying Wang


2022

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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
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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.