Clustering-Based Relevance Feedback for Web Pages

Most of traditional relevance feedback systems simply choose top ranked Web pages for a query as the source of providing the weights of candidate query expansion terms for the query. However, the whole contents of such top-ranked Web pages are usually mixed with sub-topically distinguishable contents that are too heterogeneous to be directly used to extract good quality candidate query expansion terms. In this paper, our basic idea is that the Web pages properly clustered into a sub-topic cluster can be used as a better source rather than whole given Web pages, to provide more topically coherent relevance feedback for that specific sub-topic. Thus we proposed Clustering-Based Relevance Feedback for Web Pages, which utilizes three methods to cluster given or retrieved Web pages into several subtopic-clusters. These three methods cooperates to construct good quality clusters by respectively supporting Web pages Segmentation, Terms (or Features) Selection, k Seed Centroids Selection. Here the automatically selected terms indicate the relevance feedback to construct all sub-topic clusters and assign the given Web pages into proper clusters. Each subset of the selected terms, which occurs in the Web pages assigned into a sub-topic cluster, indicates the relevance feedback to expand a query over that sub-topic cluster. Our experimental results showed that the clustering performances based on two traditional term-weighting methods (i.e., an unsupervised method and a supervised method) were significantly improved with our methods.