Mining Top - k Ranked Webpages Using SA and GA

Abstract

Searching on the Internet has grown in importance over the last few years, as huge information is invariably accumulated on the Web. The problem involves in locating the desired information and corresponding URLs on WWW. With billions of webpages in existence today, it is important to develop efficient means of locating the relevant webpages on a given topic. A single topic may have thousands of relevant pages and of varying popularity. Top - k ranked webpages pertaining to a given topic are of interest to the Web user. In this chapter, we propose an efficient top-k document retrieval method (TkRSAGA), that works on the existing search engines using the combination of Simulated Annealing and Genetic Algorithms. The Simulated Annealing is used as an optimized search technique in locating the top-k relevant webpages, while Genetic Algorithms helps in faster convergence via parallelism. Simulations are conducted on real datasets and the results indicate that TkRSAGA outperforms the existing algorithms.