Comparison of Link Based Web Page Ranking Algorithms based on Weighted Graph using Probabilistic Approach

The web today plays an important role in the cultural, educational and commercial life of millions of the users. With the huge amount of information available on the web, users typically rely on the web search engines in order to get the most desired and relevant information. As most of the users examine the first few pages so the key for user satisfaction is to give the desired results in the first few pages. Therefore the role of ranking algorithms is crucial i.e. select the pages that are most likely be able to satisfy the user's needs and also bring those results to the top positions. These ranking algorithms use a web graph as an input having crisp values. When the refined values of the web graph are used the performance of the algorithm is improved. The refined values of the web graph are obtained by calculating the conditional probability of each out links. The performance measures for the ranking algorithms are Mean Reciprocal Rank, Mean Average Precision and Normalized Discounted Cumulative Gain values. The rank values are calculated and their efficiency is compared with the present scenario of considering the crisp values of the out links to the proposed scenario of considering the conditional probabilities of out links.