Research on Improved Algorithm of PageRank Based on Vector Space

Abstract

The traditional PageRank algorithm, which is based on the link analysis algorithm, considers the randomness of user access behavior, but the relevance of the query topic is poor. In order to improve the relevance of the page data search and acquisition, this paper proposes a PageRank algorithm based on the lucent vector space scoring model. The algorithm builds a vector space model based on the web content characteristics, calculates the similarity of the subject content, and combines the original PageRank algorithm, the new PR value is obtained after weighted fusion. Experiments show that the improved algorithm reduces the number of irrelevant pages, and the PR value can better reflect the relevance of the topic.