To recommend useful microblogs that match users' interests and likes effectively

an approach in which the dynamic interests and social networking (DISN) of users are seamlessly integrated based on LDA model is proposed.The approach infers the interest vector of users better by using time function and groups the new published microblogs by clustering method and gets the best matching groups with users' interest vector.Then DISN traverses the selected groups by grid querying approach and matches the microblogs with publishers' probabilities of being followed and sorts the result.Finally the personalized microblogging recommendation is achieved.Experimental results show that DISN is more effective and efficient than the traditional models.