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