Abstract
The main algorithms at the heart of search engines have focused on ranking and classifying sites. This is appropriate when we know what we are looking for and want it directly. Alternatively, we surf, in which case ranking and classifying links becomes the focus. We address this problem using a latent semantic analysis of the web. This technique allows us to rate, suppress or create links giving us a version of the web suitable for surfing. Furthermore, we show on benchmark examples that the performance of search algorithms such as PageRank is substantially improved as they work on an appropriately weighted graph.
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References
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project (1998)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of the 9th ACM-SIAM, SODA (1998)
Lempel, R., Moran, S.: The stochastic approach for link-structure analysis (SALSA) and the TKC effect. In: 9th International WWW Conference (2000)
Jeev, K., Lassez, J.-L.: Symbolic Stochastic Systems. AMCS, 321–328 (2004)
Berry, M.W., Browne, M.: Understanding Search Engines: Mathematical Modeling and Text Retrieval. SIAM, Philadelphia (2005)
Deerwester, S., Dumais, S., Landauer, T.K., Furnas, G., Harshman, R.: Indexing by latent semantic analysis. J. Amer. Soc. Info. Sci. 41, 391–407 (1990)
Kumar, S.R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling emerging cybercommunities automatically. In: 8th International WWW Conference (1999)
Markov, A.A.: Rasprostranenie zakona bol’shih chisel na velichiny, zavisyaschie drug ot druga, Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete, 2-ya seriya, tom 15, 9 4, 135-156 (1906)
Kirchhoff, G.: Über die Auflösung der Gleichungen, auf welche man bei der untersuchung der linearen verteilung galvanischer Ströme geführt wird. Ann. Phys. Chem. 72, 497–508 (1847)
Langville, A., Meyer, C.: Google’s PageRank and Beyond: The science of search engine rankings. Princeton University Press, Princeton, New Jersey (2006)
Chandru, V., Lassez, J.-L.: Qualitative Theorem Proving in Linear Constraints, Theory and Practice, Verification, pp. 395–406 (2003)
Lassez, J.-L.: From LP to LP: Programming with Constraints. In: Ito, T., Meyer, A.R. (eds.) TACS 1991. LNCS, vol. 526, pp. 420–446. Springer, Heidelberg (1991)
Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936)
Lassez, J.-L., Karadeniz, T., Mukkamala, S.: Zoomed Clusters. ICONIP, 824–830 (2006)
Tsaparas, P.: Using non-linear dynamical systems for web searching and ranking. Principles of Database Systems, 59–69 (2004)
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Lassez, JL., Rossi, R., Jeev, K. (2008). Ranking Links on the Web: Search and Surf Engines. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_21
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DOI: https://doi.org/10.1007/978-3-540-69052-8_21
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