User interest modeling by labeled LDA with topic features

As well known, the user interest is carried in the user´s web browsing history that can be mined out. This paper presents an innovative method to extract user´s inter.ests from his/her web browsing history. We first apply an efficient algorithm to extract useful texts from the web pages in user´s browsed URL sequence. We then proposed a Labeled Latent Dirichlet Allocation with Topic Feature (LLDA-TF) to mine user´s interests from the texts. Unlike other works that need a lot of training data to train a model to adopt supervised information, we directly introduce the raw supervised information to the procedure of LLDA-TF. As shown in the experimental results, results given by LLDA-TF fit predefined categories well. Furthermore, LLDA-TF model can name the user interests by category words as well as a keyword list for each category.