A content based microblogger recommendation model

Social networks are one of the most significant information sources on the Internet. People share information, their feelings, their opinions and interesting links. A microblogging system is a special kind of social network in which users post short but frequent update messages. Microbloggers subscribe (follow) to posts of others. However, finding relevant microbloggers to follow is a major problem, due to the massive quantity of users as well as the difficulty of mentally aggregating fragmented short contributions. In this thesis, a content based recommendation model is proposed, which given a query recommends a set of ranked microbloggers. This model focuses on the content of posts as well as other characteristics of microbloggers to evaluate the relevance of microbloggers to the query. This thesis describes the model and a prototype implementation. Finally the outcome of a test with 41 users is discussed along with observations and recommendations for improved recommendations.