short-paper

Using personality to adjust diversity in recommender systems

Authors:

Liang HeAuthors Info & Claims

HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media

Pages 225 - 229

https://doi.org/10.1145/2481492.2481521

Published: 01 May 2013 Publication History

Abstract

Nowadays, although some approaches have been proposed to enhance the diversity in online recommendations, they neglect the user's spontaneous needs that might be possibly influenced by her/his personality. Previously, we did a user survey that showed some personality dimensions (such as conscientiousness which is one of personality factors according to the big-five factor model) have significant impact not only on users' diversity preference over items' individual attributes, but also on their overall diversity needs when all attributes are combined. Motivated by the findings, in the current work, we propose a strategy that explicitly embeds personality, as a moderating factor, to adjust the diversity degree within multiple recommendations. Moreover, we performed a user evaluation on the developed system. The experimental results demonstrate an effective solution to generate personality-based diversity in recommender systems.

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Cited By

Barile FCau FTintarev NBoratto LGena CMarras MGermanakos PPopescus E(2024)A Preliminary Analysis on Self and Peer Evaluation of Personality Models for Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664864(70-74)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664864
Cena FGena CMattutino CMioli MTreccani BVernero FZancanaro M(2024)Do psychological traits influence the perceived usefulness of rule recommendations in configuration tasks?Behaviour & Information Technology10.1080/0144929X.2024.2396478(1-17)Online publication date: 28-Aug-2024
https://doi.org/10.1080/0144929X.2024.2396478
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Show More Cited By

Index Terms

Using personality to adjust diversity in recommender systems
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media

May 2013

275 pages

ISBN:9781450319676

DOI:10.1145/2481492

General Chair:
Gerd Stumme
University of Kassel, Germany
,
Program Chair:
Andreas Hotho
University of Würzburg, Germany

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

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Published: 01 May 2013

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HT '13: 24th ACM Conference on Hypertext and Social Media

May 1 - 3, 2013

Paris, France

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HT '13 Paper Acceptance Rate 16 of 96 submissions, 17%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Barile FCau FTintarev NBoratto LGena CMarras MGermanakos PPopescus E(2024)A Preliminary Analysis on Self and Peer Evaluation of Personality Models for Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664864(70-74)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664864
Cena FGena CMattutino CMioli MTreccani BVernero FZancanaro M(2024)Do psychological traits influence the perceived usefulness of rule recommendations in configuration tasks?Behaviour & Information Technology10.1080/0144929X.2024.2396478(1-17)Online publication date: 28-Aug-2024
https://doi.org/10.1080/0144929X.2024.2396478
Pera MCena FLandoni MMusto CStarke A(2024)Human Factors in User Modeling for Intelligent SystemsA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_1(3-42)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_1
Kleć MWieczorkowska ASzklanny KStrus W(2023)Beyond the Big Five personality traits for music recommendation systemsEURASIP Journal on Audio, Speech, and Music Processing10.1186/s13636-022-00269-02023:1Online publication date: 19-Jan-2023
https://doi.org/10.1186/s13636-022-00269-0
Ryazantsev GOliseenko VAbramov M(2023)The Relationship between the Cattell Test and Users Social Network Subscriptions2023 XXVI International Conference on Soft Computing and Measurements (SCM)10.1109/SCM58628.2023.10159052(29-31)Online publication date: 24-May-2023
https://doi.org/10.1109/SCM58628.2023.10159052
Liu WXi YQin JDai XTang RLi SZhang WZhang R(2023)Personalized Diversification for Neural Re-ranking in Recommendation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00067(802-815)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00067
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
https://dl.acm.org/doi/10.1016/j.knosys.2023.110699
Alves GJannach Dde Souza RDamian DManzato M(2023)Digitally nudging users to explore off-profile recommendations: here be dragonsUser Modeling and User-Adapted Interaction10.1007/s11257-023-09378-734:2(441-481)Online publication date: 4-Oct-2023
https://doi.org/10.1007/s11257-023-09378-7
Lu YCui PYu LLi LZhu WZhang ARangwala H(2022)Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture ModelProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539033(3458-3467)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539033
Tang MLiao I(2022)Preference diversity and openness to novelty: Scales construction from the perspective of movie recommendationJournal of the Association for Information Science and Technology10.1002/asi.2462873:9(1222-1235)Online publication date: 25-Feb-2022
https://doi.org/10.1002/asi.24628
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