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A Two-stage Information Filtering Based on Rough Decision Rule and Pattern Mining

Xujuan Zhou1, Yuefeng Li1, Peter Bruza1, Yue Xu 1, and Raymond Lau 2
1. Faculty of Science and Technology, Queensland University of Technology, Brisbane, Australia
2. Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Abstract—Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. 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 twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Index Terms—information filtering, user profiles, rough set theory, pattern mining  

Cite: Xujuan Zhou, Yuefeng Li, Peter Bruza, Yue Xu, and Raymond Lau, "A Two-stage Information Filtering Based on Rough Decision Rule and Pattern Mining," Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 4, pp. 326-332, November 2010. doi:10.4304/jetwi.2.4.326-332
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