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• W2978889344 abstract "A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations." @default.
• W2978889344 created "2019-10-10" @default.
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• W2978889344 date "2019-09-25" @default.
• W2978889344 modified "2023-10-01" @default.
• W2978889344 title "A Model of Social Explanations for a Conversational Movie Recommendation System" @default.
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• W2978889344 doi "https://doi.org/10.1145/3349537.3351899" @default.
• W2978889344 hasPublicationYear "2019" @default.
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