Inferring user interests for passive users on Twitter by leveraging followee biographies

Abstract

User modeling based on the user-generated content of users on social networks such as Twitter has been studied widely, and has been used to provide personalized recommendations via inferred user interest profiles. Most previous studies have focused on active users who actively post tweets, and the corresponding inferred user interest profiles are generated by analyzing these users’ tweets. However, there are also a great number of passive users who only consume information from Twitter but do not post any tweets. In this paper, we propose a user modeling approach using the biographies (i.e., self descriptions in Twitter profiles) of a user’s followees (i.e., the accounts that they follow) to infer user interest profiles for passive users. We evaluate our user modeling strategy in the context of a link recommender system on Twitter. Results show that exploring the biographies of a user’s followees improves the quality of user modeling significantly compared to two state-of-the-art approaches leveraging the names and tweets of followees.