SimRank: A Page Rank approach based on similarity measure

Abstract

As the Web contains rich and convenient information, Web search engine is increasingly becoming the dominant information retrieving approach. In order to rank the query results of web pages in an effective and efficient fashion, we propose a new page rank algorithm based on similarity measure from the vector space model, called SimRank, to score web pages. Firstly, we propose a new similarity measure to compute the similarity of pages and apply it to partition a web database into several web social networks (WSNs). Secondly, we improve the traditional PageRank algorithm by taking into account the relevance of page to a given query. Thirdly, we design an efficient web crawler to download the web data. And finally, we perform experimental studies to evaluate the time efficiency and scoring accuracy of SimRank with other approaches.