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A Diary Study of Social Explanations for Recommendations in Daily Life

Published: 04 July 2022 Publication History

Abstract

We report a diary study of the explanations for the recommendations to characterize the social features in these explanations recorded by five participants over two months. The study reveals several social explanation categories (e.g., personal opinions and personal experiences) and their relationship with user contexts (e.g., location, relevant experience) and recommender attributes (e.g., integrity, expertise) illustrated in a network diagram. Specifically, personal opinions and experiences are two prominent social explanations, mainly associated with user contexts (e.g., users’ preferences and users’ experiences) and several recommender attributes (e.g., politeness, benevolence, and experience). Finally, we discuss several design implications for social explanations and anticipate the value of our findings regarding designing personalized social explanations in recommender systems that aim to build rapport with users, such as conversational recommender systems.

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Cited By

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  • (2024) Mining User Study Data to Judge the Merit of a Model for Supporting User‐Specific Explanations of AI Systems Computational Intelligence10.1111/coin.7001540:6Online publication date: 17-Dec-2024

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cover image ACM Conferences
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
July 2022
409 pages
ISBN:9781450392327
DOI:10.1145/3511047
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Published: 04 July 2022

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Author Tags

  1. Social explanations
  2. diary study
  3. recommender systems

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  • Research-article
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  • Hong Kong Research Grants Council
  • Hong Kong Baptist University IRCMS Project

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  • (2024) Mining User Study Data to Judge the Merit of a Model for Supporting User‐Specific Explanations of AI Systems Computational Intelligence10.1111/coin.7001540:6Online publication date: 17-Dec-2024

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