HierarchicalRank: Webpage Rank Improvement Using HTML TagLevel Similarity

In the past researches, two types of algorithms are introduced that are query dependent and query independent, works online or offline. PageRank Algorithm works offline independent to query while Hyperlink-Induced Topic Search (HITS) algorithm woks online dependent on query. One of the problems of these algorithms is that, division of the rank is based on number of inlinks, outlinks and different parameters used in hyperlink analysis which is dependent or independent to webpage content with the problem of topic drift. Previous researches were focused to solve this problem using the popularity of the outlink webpages. In this paper a novel algorithm for popularity measure is proposed based on similarity between query and Hierarchical text extracted from source and target webpage using Hyper Text Markup Language (HTML) tags importance parameter. In this paper, result of proposed method is compared with PageRank Algorithm and Topic Distillation with Query Dependent Link Connections and Page Characteristics results.