A pattern mining approach for information filtering systems

It is a big challenge to clearly identify the boundary between positive and negative\udstreams for information filtering systems. Several attempts have used negative feedback to\udsolve this challenge; however, there are two issues for using negative relevance feedback to\udimprove the effectiveness of information filtering. The first one is how to select constructive\udnegative samples in order to reduce the space of negative documents. The second issue is\udhow to decide noisy extracted features that should be updated based on the selected negative\udsamples. This paper proposes a pattern mining based approach to select some offenders\udfrom the negative documents, where an offender can be used to reduce the side effects of\udnoisy features. It also classifies extracted features (i.e., terms) into three categories: positive\udspecific terms, general terms, and negative specific terms. In this way, multiple revising\udstrategies can be used to update extracted features. An iterative learning algorithm is also\udproposed to implement this approach on the RCV1 data collection, and substantial experiments\udshow that the proposed approach achieves encouraging performance and the performance\udis also consistent for adaptive filtering as well