Enabling the analysis of personality aspects in Recommender Systems

Shahpar Yakhchi, Amin Beheshti, Seyed Mohssen Ghafari, Mehmet Orgun

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

Abstract

Existing Recommender Systems mainly focus on exploiting users’ feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users’ personal interests and level of knowledge, as a key factor to increase recommendations’ acceptance. Differently, we identifying users’ personality type implicitly with no burden on users and incorporate it along with users’ personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.
Original languageEnglish
Title of host publicationPacific Asia Conference on Information Systems (PACIS 2019)
EditorsDongming Xu, James Jiang, Hee-Woong Kim
Place of PublicationAtlanta, Ga
PublisherAssociation for Information Systems
Pages1-14
Number of pages14
Publication statusPublished - 2019
Event23rd Pacific-Asia Conference on Information Systems, PACIS 2019 - Xi'an, China
Duration: 8 Jul 201912 Jul 2019

Conference

Conference23rd Pacific-Asia Conference on Information Systems, PACIS 2019
CountryChina
CityXi'an
Period8/07/1912/07/19

Keywords

  • Recommender Systems
  • personality traits
  • Collaborative Filtering

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  • Cite this

    Yakhchi, S., Beheshti, A., Ghafari, S. M., & Orgun, M. (2019). Enabling the analysis of personality aspects in Recommender Systems. In D. Xu, J. Jiang, & H-W. Kim (Eds.), Pacific Asia Conference on Information Systems (PACIS 2019) (pp. 1-14). Atlanta, Ga: Association for Information Systems.