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How personality influences users' needs for recommendation diversity?

Authors:

Li Chen,

Wen Wu,

Liang HeAuthors Info & Claims

CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

Pages 829 - 834

https://doi.org/10.1145/2468356.2468505

Published: 27 April 2013 Publication History

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Abstract

The existing approaches for enhancing diversity in online recommendations neglect the user's spontaneous needs that might be potentially influenced by her/his personality. In this paper, we report our ongoing research on exploring the actual impact of personality values on users' needs for recommendation diversity. The results from a preliminary user survey are reported, that show the significantly causal relationship from personality factors (such as conscientiousness) to the users' diversity preference (not only over the item's individual attributes but also on all attributes when they are combined). We further present our plan for the follow-up work and discuss its practical implications.

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Cited By

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Nguyen KNguyen NNgo NTran NNguyen H(2024)Investigating Consumers’ Purchase Resistance Behavior to AI-Based Content Recommendations on Short-Video Platforms: A Study of Greedy And Biased RecommendationsJournal of Internet Commerce10.1080/15332861.2024.237596623:3(284-327)Online publication date: 11-Jul-2024
https://doi.org/10.1080/15332861.2024.2375966
Yang JLi HZhang LXu G(2024)Coping Responses to the Stress of Using News Platforms’ Recommendation AlgorithmsInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2352937(1-16)Online publication date: 2-Jul-2024
https://doi.org/10.1080/10447318.2024.2352937
Dodeja LTambwekar PHedlund-Botti EGombolay M(2024)Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasksInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103216184(103216)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijhcs.2023.103216
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Index Terms

How personality influences users' needs for recommendation diversity?
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information & Contributors

Information

Published In

CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

April 2013

3360 pages

ISBN:9781450319522

DOI:10.1145/2468356

General Chair:
Wendy E. Mackay
INRIA
,
Program Chairs:
Stephen Brewster
Glasgow University
,
Susanne Bødker
University of Aarhus

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 April 2013

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CHI '13

Sponsor:

SIGCHI

CHI '13: CHI Conference on Human Factors in Computing Systems

April 27 - May 2, 2013

Paris, France

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CHI EA '13 Paper Acceptance Rate 630 of 1,963 submissions, 32%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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Cited By

View all

Nguyen KNguyen NNgo NTran NNguyen H(2024)Investigating Consumers’ Purchase Resistance Behavior to AI-Based Content Recommendations on Short-Video Platforms: A Study of Greedy And Biased RecommendationsJournal of Internet Commerce10.1080/15332861.2024.237596623:3(284-327)Online publication date: 11-Jul-2024
https://doi.org/10.1080/15332861.2024.2375966
Yang JLi HZhang LXu G(2024)Coping Responses to the Stress of Using News Platforms’ Recommendation AlgorithmsInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2352937(1-16)Online publication date: 2-Jul-2024
https://doi.org/10.1080/10447318.2024.2352937
Dodeja LTambwekar PHedlund-Botti EGombolay M(2024)Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasksInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103216184(103216)Online publication date: Apr-2024
https://doi.org/10.1016/j.ijhcs.2023.103216
Wester JDe Jong SPohl HVan Berkel N(2024)Exploring People’s Perceptions of LLM-generated AdviceComputers in Human Behavior: Artificial Humans10.1016/j.chbah.2024.100072(100072)Online publication date: Jun-2024
https://doi.org/10.1016/j.chbah.2024.100072
Tkalčič MMotamedi E(2024)Inferring Eudaimonia and Hedonia from Digital TracesA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_6(165-182)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_6
Rad DCuc LLile RCuc PPantea MAnta D(2023)Suspiciousness and Fast and Slow Thinking Impact on Trust in Recommender SystemsProceedings of the International Conference on Business Excellence10.2478/picbe-2023-009917:1(1103-1118)Online publication date: 14-Jul-2023
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Shen DZhu HXiao KZhang XXiong H(2023)Exploiting Connections among Personality, Job Position, and Work Behavior: Evidence from Joint Bayesian LearningACM Transactions on Management Information Systems10.1145/360787514:3(1-20)Online publication date: 12-Sep-2023
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Yang SZhang LXia FLin LFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614688(4901-4908)Online publication date: 21-Oct-2023
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Yan DChen L(2023)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 10-Mar-2023
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De Biasio AMonaro MOneto LBallan LNavarin N(2023)On the problem of recommendation for sensitive users and influential itemsKnowledge-Based Systems10.1016/j.knosys.2023.110699275:COnline publication date: 5-Sep-2023
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