Mining user interest and its evolution for recommendation on the micro-blogging system

Abstract

Different users have different needs, it is increasingly difficult to recommend interested topics to them. The micro-blogging system can expose user interests from individual behaviors along with his/her social connections. It also offers an opportunity to investigate how a large-scale social system recommends personal preferences according to the temporal, spatial and topical aspects of users activity. Here we focus on the problem of mining user interest and modeling its evolution on the micro-blogging system for recommendation. We learn the user preference on topics from the visited micro-bloggings as user interest using text mining techniques. We then extend this concept with user's social connection on different topics. Moreover, we study the evolution of the user interest model and finally recommend the most preferred micro-bloggings to a user. Experiments on a large scale of micro-blogging dataset shows that our model outperforms traditional approaches and achieves considerable performance on recommending interested posts to a user. © 2013 Springer-Verlag Berlin Heidelberg.