Feature discovery in relevance feedback using pattern mining

It is a big challenge to guarantee the quality of extracted features in text documents to describe user interests or preferences due to large amounts of noise. Over the years, pattern mining-based approaches to RF have attracted great interest to discover knowledge of user interest from text documents. However, the data mining approaches often produce a large set of patterns, which include a lot of noisy patterns, reducing the effective use of pattern mining. In this paper, we present a novel pattern mining approach to RF. This approach mines patterns in both positive and negative feedback and then classifies them into clusters to find user-specific patterns. We also propose a novel pattern deploying method that effectively uses the discovered patterns for improving the performance of searching relevant documents. Experiments are conducted on Reuters Corpus Volume 1 data collection (RCV1) and TREC filtering topics. The results show that the proposed approach achieves promising performance comparing with state-of-the-art term-based methods and pattern-based ones.