A New Hybrid Method for Web Pages Ranking in Search Engines

There are many algorithms for optimizing the search engine results, ranking takes place according to one or more parameters such as; Backward Links, Forward Links, Content, click through rate and etc. The quality and performance of these algorithms depend on the listed parameters. The ranking is one of the most important components of the search engine that represents the degree of the vitality of a web page. It also examines the relevance of search results with the user's query. In this paper, we try to optimize the search engine results ranking by using the hybrid of the structure-based algorithms (Distance Rank algorithm) and user feedback-based algorithms (Time Rank algorithm). The proposed method acts on multiple parameters and with more parameters it tries to get better results while keeping the complexity and running time of the algorithms. Average distance and average attention time have been evaluated on web pages and by using the obtained data, proposed method performance has been evaluated. We compare proposed method with several famous algorithms such as Time Rank, Page Rank, R Rank, WPR and sNorm(p) in this field by applying Precision@N (P@N), Average Precision (AP), Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized Discounted Cumulative Gain (NDCG) criteria. The results indicate better performance in comparison with existing algorithms.