A Diary Study of Social Explanations for Recommendations in Daily Life

Zhirun Zhang; Yucheng Jin; Li Chen

doi:10.1145/3511047.3537681

A Diary Study of Social Explanations for Recommendations in Daily Life

Zhirun Zhang, Yucheng Jin, Li Chen

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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.

Original language	English
Title of host publication	UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association for Computing Machinery (ACM)
Pages	200–208
Number of pages	9
ISBN (Print)	9781450392327
DOIs	https://doi.org/10.1145/3511047.3537681
Publication status	Published - 4 Jul 2022
Event	30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2022 - Barcelona, Spain Duration: 4 Jul 2022 → 7 Jul 2022 https://www.um.org/umap2022/ https://dl.acm.org/doi/proceedings/10.1145/3511047

Publication series

Name	Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference	30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2022
Country/Territory	Spain
City	Barcelona
Period	4/07/22 → 7/07/22
Internet address	https://www.um.org/umap2022/ https://dl.acm.org/doi/proceedings/10.1145/3511047

Scopus Subject Areas

Software

User-Defined Keywords

Social explanations
diary study
recommender systems

Access to Document

10.1145/3511047.3537681

Cite this

Zhang, Z., Jin, Y., & Chen, L. (2022). A Diary Study of Social Explanations for Recommendations in Daily Life. In UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 200–208). (Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery (ACM). https://doi.org/10.1145/3511047.3537681

Zhang, Zhirun ; Jin, Yucheng ; Chen, Li. / A Diary Study of Social Explanations for Recommendations in Daily Life. UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), 2022. pp. 200–208 (Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization).

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title = "A Diary Study of Social Explanations for Recommendations in Daily Life",

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{\textquoteright} preferences and users{\textquoteright} 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.",

keywords = "Social explanations, diary study, recommender systems",

author = "Zhirun Zhang and Yucheng Jin and Li Chen",

note = "Funding Information: The work was supported by Hong Kong Research Grants Council (RGC/HKBU12201620) and Hong Kong Baptist University IRCMS Project (IRCMS/19-20/D05). We also thank all participants for their time in taking part in the study and reviewers for their constructive comments on our paper. Publisher Copyright: {\textcopyright} 2022 ACM.; 30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2022 ; Conference date: 04-07-2022 Through 07-07-2022",

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Zhang, Z, Jin, Y & Chen, L 2022, A Diary Study of Social Explanations for Recommendations in Daily Life. in UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery (ACM), pp. 200–208, 30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2022, Barcelona, Spain, 4/07/22. https://doi.org/10.1145/3511047.3537681

A Diary Study of Social Explanations for Recommendations in Daily Life. / Zhang, Zhirun; Jin, Yucheng; Chen, Li.
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM), 2022. p. 200–208 (Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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T1 - A Diary Study of Social Explanations for Recommendations in Daily Life

AU - Zhang, Zhirun

AU - Jin, Yucheng

AU - Chen, Li

N1 - Funding Information: The work was supported by Hong Kong Research Grants Council (RGC/HKBU12201620) and Hong Kong Baptist University IRCMS Project (IRCMS/19-20/D05). We also thank all participants for their time in taking part in the study and reviewers for their constructive comments on our paper. Publisher Copyright: © 2022 ACM.

PY - 2022/7/4

Y1 - 2022/7/4

N2 - 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.

AB - 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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BT - UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization

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Y2 - 4 July 2022 through 7 July 2022

ER -

Zhang Z, Jin Y, Chen L. A Diary Study of Social Explanations for Recommendations in Daily Life. In UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery (ACM). 2022. p. 200–208. (Adjunct Proceedings of ACM Conference on User Modeling, Adaptation and Personalization). doi: 10.1145/3511047.3537681

A Diary Study of Social Explanations for Recommendations in Daily Life

Abstract

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Scopus Subject Areas

User-Defined Keywords

Access to Document

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