Content Based Microblogger Recommendation

Micro logs are social networking systems, where users frequently contribute very short (micro) posts. Micro loggers follow others´ posts via subscription. However, it is a challenge to determine micro loggers worthy of following, since their contributions are fragmented amidst numerous and various tiny posts. Furthermore, micro posts typically include abbreviations, mispeled words, special tokens, and are not grammatically correct. Consequently, typical NLP techniques are often not very useful in determining what a user posts about. This work proposes a content-based, as opposed to friendship-based, micro blogger recommendation model, where given an user query a ranked list of microbloggers is produced. This paper presents the recommendation model, a prototype implementation, an user evaluation.