Enhancing Accuracy of Topic Sensitive PageRank using Jaccard Index and Cosine Similarity

The importance of online information retrieval systems has dramatically increased through considerable growth in the size of the web, and the challenges beyond this topic have become a center of attention for many researchers. This remarkable growth in the size of the web has led to in-depth studies on every element of information retrieval systems such as web page ranking algorithms. The accuracy of these algorithms plays a critical role in the search engines, whereas the ranker is responsible for accuracy. Thus, the ranker is a principal module of each search engine. In this paper, a new framework based on web graph and content similarity is presented in order to improve the accuracy of PageRank. This framework is implemented using Jaccard index and cosine similarity measures, and as a result of our empirical analysis, we shall show that putting page content similarity in action increases the accuracy of web ranking in some candidate ranking algorithms. In addition, time complexity and implementation issues are discussed to achieve a practical result.