Department of Computer Science
Using personality to adjust diversity in recommender systems
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. Copyright 2013 ACM.
Diversity, Personality-based recommender systems, User evaluation
Source Publication Title
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Wu, Wen, Li Chen, and Liang He. "Using personality to adjust diversity in recommender systems." Proceedings of the 24th ACM Conference on Hypertext and Social Media (2013): 225-229.