Pattern Mining for a Two-Stage Information Filtering System

As information available over computer networks is growing exponentially, searching for useful information becomes increasingly more difficult. Accordingly, developing an effective information filtering mechanism is becoming very important to alleviate the problem of information overload. Information filtering systems often employ user profiles to represent users' information needs so as to determine the relevance of documents from an incoming data stream. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed two-stage filtering model significantly outperforms both the term-based and pattern-based information filtering models.