A General Model for Mutual Ranking Systems
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Ranking has been applied in many domains using recommendation systems such as search engine, e-commerce, and so on. We will introduce and study N-linear mutual ranking, which can rank n classes of objects at once. The ranking scores of these classes are dependent to the others. For instance, PageRank by Google is a 2-linear mutual ranking, which ranks the webpages and links at once. Particularly, we focus to N-star ranking model and demonstrate it in ranking conference and journal problems. We have conducted the experiments for the models in which the citations are not considered. The experimental results are based on the DBLP dataset, which contains more than one million papers, authors and thousands of conferences and journals in computer science. Finally, N-star ranking is a very strong ranking algorithm can be applied in many real-world problems.
KeywordsN-star ranking Markov chain PageRank Academic ranking Conference ranking Ranking algorithms Prolific ranking Recommendation systems Bibliographical database DBLP
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- 1.Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the 7th International World Wide Web Conference, pp. 107–117 (1998)Google Scholar
- 5.Ley, M., Reuther, P.: Maintaining an Online Bibliographical Database: The Problem of Data Quality. EGC 2006, 5–10 (2006)Google Scholar
- 7.Furukawa, T., Okamoto, S., Matsuo, Y., Ishizuka, M.: Prediction of social bookmarking based on a behavior transition model. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1741–1747 (2010)Google Scholar
- 8.Rendle, S., Freudenthaler, C., Thieme, L.S.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)Google Scholar
- 9.Freudenthaler, C., Rendle, S., Thieme, L.S.: Factorizing Markov Models for Categorical Time Series Prediction. In: Proceedings of ICNAAM, pp. 405–409 (2011)Google Scholar
- 10.Microsoft Corporation: Microsoft Academic Search (June 26, 2013), http://academic.research.microsoft.com/
- 11.Collins, M.: Ranking algorithms for named-entity extraction: Boosting and the voted perceptron. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 489–496 (2002)Google Scholar
- 12.Vercoustre, A.-M., Thom, J.A., Pehcevski, J.: Entity ranking in Wikipedia. In: Proceedings of the 2008 ACM Symposium on Applied Computing (SAC 2008), pp. 1101–1106 (2008)Google Scholar
- 14.Nie, Z., Zhang, Y., Wen, J., Ma, W.: Object-Level Ranking: Bringing Order to Web Objects, Study of the eXplicit Control Protocol (XCP). IEEE Infocom (2005)Google Scholar
- 15.Snchez-Burillo, E., Duch, J., Gmez-Gardenes, J., Zueco, D.: Quantum Navigation and Ranking in Complex Networks Nature online journal (2012)Google Scholar