With the fast development of web 2.0, user-centric publishing and knowledge management platforms, such as Wiki, Blogs, and Q & A systems attract a large number of users. Given the availability of the huge amount of meaningful user generated content, incremental model based recommendation techniques can be employed to improve users' experience using automatic recommendations. In this paper, we propose an incremental recommendation algorithm based on Probabilistic Latent Semantic Analysis (PLSA). The proposed algorithm can consider not only the users' long-term and short-term interests, but also users' negative and positive feedback. We compare the proposed method with several baseline methods using a real-world Question & Answer website called Wenda. Experiments demonstrate both the effectiveness and the efficiency of the proposed methods.
['Hu Wu', 'Yongji Wang', 'Xiang Cheng']
Incremental probabilistic latent semantic analysis for automatic question recommendation