Page Ranking Algorithm Based On Link Similarity Study

This paper focuses on the relevant page sorting algorithms. We discuss the link analysis technique with emphasis.First of all, we have introduced the basic principle of page sorting algorithms. We have carried on the contrastive analysis to several kinds of more commonly used page sorting technologies. We have analyzed two kinds of typical link analysis algorithm emphatically: PageRank and HITS, and have analyzed their respective advantages and disadvantages.A major flaw of the PageRank algorithm is that the algorithm distributes the PageRank value to all out-links equally. It does not consider the semantic information very well, so it will be influenced by the irrelevant links and bring the subject drifting.In this paper, we design a simple model to improve PageRank algorithm. We consider the similarity of links based on the original PageRank algorithm with average distribution and evaluate the link similarity with the naive Bayesian model. With consideration of the similarity between the link and the target page, we give less PageRank to those pages with less value (such as advertisement pages), and promote the PageRank of truly valuable pages.Finally, we construct a simulative search engine with the improved model above. The simulation system includes almost all of the features of a search engine. We invite some users to test the system in the real Internet environment for validation. The small-scale test results show that it enhances the customer satisfaction when we use the link similarity.