Data Selection for User Topic Model in Twitter-Like Service

Twitter-like services are now a popular kind of online social networking services, in which user can express themselves, share contents, and follow others they are interested in. User modeling, building a model for user's interests, is a key problem in many social networking applications, such as recommendation, advertisement, etc. This paper focuses on data selection for user modeling in Twitter-like services. That is, we study the problem of how to select useful data to model a user's interests. Using different data, three user modeling methods are proposed and experiments on a real Twitter-like service are conducted to verify the effectiveness of proposed approaches. Experimental results shows that modeling user's interests with what he/she wrote and selectively what he read performs the best among the three methods we proposed.