Improving user profile with personality traits predicted from social media content

Authors:
Rui Gao;Bibo Hao;Shuotian Bai;Lin Li;Ang Li;Tingshao Zhu
Affiliations:
University of Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;Institute of Psychology Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 7th ACM conference on Recommender systems
Year:
2013

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Abstract

Existing studies indicate that there exists strong correlation between personality and personal preference, thus personality could potentially be used to build more personalized recommender system. Personality traits are mainly measured by psychological questionnaires, and it is hard to obtain personality traits of large amount of users in real-world scenes.In this paper, we propose a new approach to automatically identify personality traits with Social Media contents in Chinese language environments. Social Media content features were extracted from 1766 Sina micro blog users, and the predicting model is trained with machine learning algorithms.The experimental results demonstrate that users' personality traits could be predicted from Social Media contents with acceptable Pearson Correlation, which makes it possible to develop user profiles for recommender system. In future, user profiles with predicted personality traits would be used to enhance the performance of existing personalized recommendation systems.