Exploring the value of personality in predicting rating behaviors: A study of category preferences on movielens

Raghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan

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

12 Scopus citations

Abstract

Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.

Original languageEnglish (US)
Title of host publicationRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages139-142
Number of pages4
ISBN (Electronic)9781450340359
DOIs
StatePublished - Sep 7 2016
Event10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States
Duration: Sep 15 2016Sep 19 2016

Publication series

NameRecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems

Other

Other10th ACM Conference on Recommender Systems, RecSys 2016
CountryUnited States
CityBoston
Period9/15/169/19/16

Bibliographical note

Funding Information:
This research was supported by the National Science Foundation under grant IIS-1319382. Additionally, we thank the MovieLens users who took the Personality survey.

Fingerprint Dive into the research topics of 'Exploring the value of personality in predicting rating behaviors: A study of category preferences on movielens'. Together they form a unique fingerprint.

Cite this