TY - GEN
T1 - E-ConvRec
T2 - 13th International Conference on Language Resources and Evaluation Conference, LREC 2022
AU - Jia, Meihuizi
AU - Liu, Ruixue
AU - Wang, Peiying
AU - Song, Yang
AU - Xi, Zexi
AU - Li, Haobin
AU - Shen, Xin
AU - Chen, Meng
AU - Pang, Jinhui
AU - He, Xiaodong
N1 - Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Conversational Recommendation
KW - Dialogue Corpus
KW - User Preference Recognition
UR - http://www.scopus.com/inward/record.url?scp=85144425433&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85144425433
T3 - 2022 Language Resources and Evaluation Conference, LREC 2022
SP - 5787
EP - 5796
BT - 2022 Language Resources and Evaluation Conference, LREC 2022
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
Y2 - 20 June 2022 through 25 June 2022
ER -