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Relating Newcomer Personality to Survival and Activity in Recommender Systems

Authors:

Raghav Pavan Karumur,

Joseph A. KonstanAuthors Info & Claims

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Pages 195 - 205

https://doi.org/10.1145/2930238.2930246

Published: 13 July 2016 Publication History

Abstract

In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.

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Cited By

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
Dhelim SAung NBouras MNing HCambria E(2021)A survey on personality-aware recommendation systemsArtificial Intelligence Review10.1007/s10462-021-10063-755:3(2409-2454)Online publication date: 19-Sep-2021
https://doi.org/10.1007/s10462-021-10063-7
Karumur RYu BZhu HKonstan JMandryk RHancock MPerry MCox A(2018)Content is King, Leadership LagsProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174080(1-13)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3174080
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Index Terms

Relating Newcomer Personality to Survival and Activity in Recommender Systems
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

July 2016

366 pages

ISBN:9781450343688

DOI:10.1145/2930238

General Chairs:
Julita Vassileva
University of Saskatchewan, Canada
,
James Blustein
Dalhousie University, Canada
,
Program Chairs:
Lora Aroyo
VU Amsterdam, the Netherlands
,
Sidney D'Mello
University of Notre Dame, USA

Copyright © 2016 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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Published: 13 July 2016

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UMAP '16: User Modeling, Adaptation and Personalization Conference

July 13 - 17, 2016

Nova Scotia, Halifax, Canada

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UMAP '16 Paper Acceptance Rate 21 of 123 submissions, 17%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

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
Dhelim SAung NBouras MNing HCambria E(2021)A survey on personality-aware recommendation systemsArtificial Intelligence Review10.1007/s10462-021-10063-755:3(2409-2454)Online publication date: 19-Sep-2021
https://doi.org/10.1007/s10462-021-10063-7
Karumur RYu BZhu HKonstan JMandryk RHancock MPerry MCox A(2018)Content is King, Leadership LagsProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3174080(1-13)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3174080
Karumur RNguyen TKonstan J(2018)Personality, User Preferences and Behavior in Recommender systemsInformation Systems Frontiers10.1007/s10796-017-9800-020:6(1241-1265)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10796-017-9800-0
Karumur RNguyen TKonstan JSen SGeyer WFreyne JCastells P(2016)Exploring the Value of Personality in Predicting Rating BehaviorsProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959140(139-142)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959140

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