Relating newcomer personality to survival and activity in recommender systems

Raghav Pavan Karumur, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages195-205
Number of pages11
ISBN (Electronic)9781450343701
DOIs
StatePublished - Jul 13 2016
Event24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016 - Halifax, Canada
Duration: Jul 13 2016Jul 17 2016

Publication series

NameUMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization

Other

Other24th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2016
CountryCanada
CityHalifax
Period7/13/167/17/16

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation under the grant IIS-13-19382. We thank Tien T. Nguyen of GroupLens Research for providing us this dataset and the MovieLens users who took the personality survey. We also thank the anonymous reviewers for their valuable comments.

Keywords

  • Big-Five Personality Traits
  • New users
  • Newcomer engagement
  • Newcomer retention
  • Personality
  • Recommender systems

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