The main goal of ranking web pages is to find the interrelated pages. This interrelation of pages is represented as weighted graph. Ranking nodes on graphs is a fundamental task in information retrieval, data mining, and social network analysis. Many existing diversified ranking algorithms either cannot be scalable to large number of nodes due to time and memory requirements or reasonable diversified ranking measure. Graph-based algorithms are stationary distribution of the random walk on the graph. Diversified ranking measure was proposed for large graphs to identify the relevance and diversity among nodes. This measure is a nondecreasing submodular set maximization problem. To solve this problem an efficient greedy algorithm was developed with linear time and memory requirements, and the size of the graph to achieve near-optimal solution. This paper presents a system that provides users with personalized results derived from a search engine that uses link structures. The proposed system uses fuzzy document retrieval (constructed from a fuzzy concept network based on the user’s profile) that personalizes the results returned from link-based search engines with the preferences of the specific users. Experimental results show that the developed method is scalable for large graph with linear time and space complexity and it has considerable efficiency in determining the rank of web pages.
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