iPLUG: Personalized List Recommendation in Twitter

A Twitter user can easily be overwhelmed by flooding tweets from her followees, making it challenging for the user to find interesting and useful information in tweets. The feature of Twitter Lists allows users to organize their followees into multiple subsets for selectively digesting tweets. However, this feature has not received wide reception because users are reluctant to invest initial efforts in manually creating lists. To address the challenge of bootstrapping Twitter Lists, we envision a novel tool that automatically creates personalized Twitter Lists and recommends them to users. Compared with lists created by real Twitter users, the lists generated by our algorithms achieve 73.6% similarity.