On-line Hot Topic Recommendation Using Tolerance Rough Set Based Topic Clustering

Abstract

In this paper we present our research of online hot topic detection and label extraction method for our hot topic recommendation system. Using a new topical feature selection method, the feature space is compressed suitable for an online system. The tolerance rough set model is used to enriching the small set of topical feature words to a topical approximation space. According to the distance defined on the topical approximation space, the web pages are clustered into groups which will be merged with document overlap. The topic labels are extracted based on the approximation topical space enriched with the useful but high frequency topical words dropped by the clustering process. The experiments show that our method could generate more information abundant classes and more topical class labels, alleviate the topical drift caused by the non-topical and noise words.