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• W2972156721 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." @default.
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• W2972156721 date "2019-01-01" @default.
• W2972156721 modified "2023-10-16" @default.
• W2972156721 title "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences" @default.
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• W2972156721 doi "https://doi.org/10.18653/v1/w19-5941" @default.
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