Using personality to adjust diversity in recommender systems

Wen Wu, Li CHEN, Liang He

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

52 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationHT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media
Pages225-229
Number of pages5
DOIs
Publication statusPublished - 2013
Event24th ACM Conference on Hypertext and Social Media, HT 2013 - Paris, France
Duration: 1 May 20133 May 2013

Publication series

NameHT 2013 - Proceedings of the 24th ACM Conference on Hypertext and Social Media

Conference

Conference24th ACM Conference on Hypertext and Social Media, HT 2013
Country/TerritoryFrance
CityParis
Period1/05/133/05/13

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • Diversity
  • Personality-based recommender systems
  • User evaluation

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