Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences

Filip Radlinski, Krisztian Balog, Bill Byrne, Karthik Krishnamoorthi

Abstract
Conversational recommendation has recently attracted significant attention. As systems must understand users’ preferences, training them has called for conversational corpora, typically derived from task-oriented conversations. We observe that such corpora often do not reflect how people naturally describe preferences. We present a new approach to obtaining user preferences in dialogue: Coached Conversational Preference Elicitation. It allows collection of natural yet structured conversational preferences. Studying the dialogues in one domain, we present a brief quantitative analysis of how people describe movie preferences at scale. Demonstrating the methodology, we release the CCPE-M dataset to the community with over 500 movie preference dialogues expressing over 10,000 preferences.
Anthology ID:
W19-5941
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
353–360
Language:
URL:
https://aclanthology.org/W19-5941
DOI:
10.18653/v1/W19-5941
Bibkey:
Cite (ACL):
Filip Radlinski, Krisztian Balog, Bill Byrne, and Karthik Krishnamoorthi. 2019. Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 353–360, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences (Radlinski et al., SIGDIAL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/W19-5941.pdf
Data
CCPE-M, Coached Conversational Preference Elicitation
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