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 language||English (US)|
|Title of host publication||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||4|
|State||Published - Sep 7 2016|
|Event||10th ACM Conference on Recommender Systems, RecSys 2016 - Boston, United States|
Duration: Sep 15 2016 → Sep 19 2016
|Name||RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems|
|Other||10th ACM Conference on Recommender Systems, RecSys 2016|
|Period||9/15/16 → 9/19/16|
Bibliographical noteFunding 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.