Paper Title
Mining User Aware Rare Sequential Topic Pattrens In Document Streams

Documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users. Document streams are created and distributed in various forms on the Internet, such as news streams, emails, micro-blog articles, chatting messages, research paper archives, web forum discussions, and so forth. The contents of these documents generally concentrate on some specific topics, which reflect offline social events and users’ characteristics in real life. To mine these pieces of information, a lot of researches of text mining focused on extracting topics from document collections and document streams through various probabilistic topic models. Keywords - UARSTP Recommendation System, Data Mining, Information Retrieval.