User interest prediction in Microblog using recommendation method
Jiantao Zhao, Ning Shi · 2014
Microblog, like Twitter and Sina Weibo, produce large content everyday by millions of user, which reflect user interest. Grasping user interest is important for content recommendation and ad targeting. In this paper, We take a novel method to predict user interest by using automatic topic learning and recommendation method. We crawl lots of user tweet data from Weibo, and use Latent Dirichlet Allocation (LDA) topic model to exact topics. We assign each tweet to one topic, then get a User-Interest matrix by accumulating each user's preference for topics. Finally we use singular value decomposition (SVD) method to predict user preference for the topics that user may interest. We evaluate our method on test data and get state of the art performance.