User Modeling on Twitter with Exploiting Explicit Relationships for Personalized Recommendations

The use of social networks sites has led to a challenging overload of information that helped new social networking sites such as Twitter to become popular. It is believed that Twitter provides a rich environment for shared information that can help with recommender systems research. In this paper, we study Twitter user modeling by utilizing explicit relationships among users. This work aims to build personal profiles through a alternative methods using information gained from Twitter to provide more accurate recommendations. Our method exploits the explicit relationships of a Twitter user to extract information that is important in building the user’s personal profile. The usefulness of this proposed method is validated by implementing a tweet recommendation service and by performing offline evaluation. We compare our proposed user profiles against other profiles such as a baseline using cosine similarity measures to check the effectiveness of the proposed method. The performance is measured on an adequate number of users.