Paper Info
Title
Improving user profile with personality traits predicted from social media content
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.
Year
DOI
Venue
2013
10.1145/2507157.2507219
RecSys
Keywords
Field
DocType
social media content,social media content feature,user profile,chinese language environment,sina micro blog user,recommender system,acceptable pearson correlation,improving user profile,personalized recommender system,personality trait,personalized recommendation system,social network,text analysis,social media,personality
Big Five personality traits,Pearson product-moment correlation coefficient,User profile,Social network,Computer science,Artificial intelligence,Recommender system,World Wide Web,Social media,Information retrieval,Microblogging,Machine learning,Personality
Conference
Citations 
PageRank 
References 
16
0.65
10
Authors
6
Name
Order
Citations
PageRank
Rui Gao1533.67
Bibo Hao2576.56
Shuotian Bai3525.76
Lin Li4201.74
Ang Li5425.37
Tingshao Zhu619233.61