Weighted PageRank Algorithm Search Engine Ranking Model for Web Pages

Abstract

As data grows in size, search engines face new challenges in extracting more relevant content for users’ searches. As a result, a number of retrieval and ranking algorithms have been employed to ensure that the results are relevant to the user’s requirements. Unfortunately, most existing indexes and ranking algorithms crawl documents and web pages based on a limited set of criteria designed to meet user expectations, making it impossible to deliver exceptionally accurate results. As a result, this study investigates and analyses how search engines work, as well as the elements that contribute to higher ranks. This paper addresses the issue of bias by proposing a new ranking algorithm based on the PageRank (PR) algorithm, which is one of the most widely used page ranking algorithms We propose weighted PageRank (WPR) algorithms to test the relationship between these various measures. The Weighted Page Rank (WPR) model was used in three distinct trials to compare the rankings of documents and pages based on one or more user preferences criteria. The findings of utilizing the Weighted Page Rank model showed that using multiple criteria to rank final pages is better than using only one, and that some criteria had a greater impact on ranking results than others.