Dynamic User Attribute Discovery on Social Media

Social media service defines a new paradigm of people communicating, self-expressing and sharing on the Web. Users in today's social media platforms often post contents, inferring their interests/attributes, which are significant for manyWeb services such as social recommendation, personalized searching and online advertising. User attributes are temporally dynamic along with internal interest changing and external influence. Based on topic modeling, we present a probabilistic method for dynamic user attribute discovery. Our method automatically detects user attributes and models the dynamics using time windows and decay function, thereby facilitating more accurate recommendation. Evaluation on a Sina Weibo dataset shows the superiority in terms of precision, recall and F-measure as compared to baselines, such as static user attribute modeling.