Randomized algorithms
Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Link analysis ranking
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
ACM Transactions on Internet Technology (TOIT)
Hits on the web: how does it compare?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Comparing the effectiveness of hits and salsa
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Faster Ranking Using Extrapolation Techniques
International Journal of Computer Vision and Image Processing
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Relevance is a numerical score assigned to a search result, representing how well the results meet the information needs of the user that issued the search query. Several mathematical tools and techniques have been used in research for improving the relevancy ranking models. Advanced concepts in linear algebra, such as the Singular Value Decomposition, and theory of Markov chains have also been employed for innovating relevancy ranking. This study presents the use of Extrapolation technique to speedup the convergence of query-dependent Link Analysis Ranking Algorithms. It contains a novel improvement in algorithms like HITS, SALSA and their descendants (e.g., Exponentiated and Randomized HITS) using the Extrapolation techniques. Using this approach it is possible to accelerate the algorithms in terms of reducing the number of iterations and therefore uncovered a much faster convergence. In the experiments we even got much better results than theoretically predicted. The results present a speedup to the order of 3-19 times.