A framework to compute page importance based on user behaviors

Information Retrieval

Volumn 13, Issue 1, 2010, Pages 22-45

  Liu, Yuting ^a   Liu, Tie Yan ^b   Gao, Bin ^b   Ma, Zhiming ^c   Li, Hang ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

^c CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

BrowseRank; Continuous time time homogeneous Markov process; Staying time; User browsing process

Indexed keywords

EID: 76349111820     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-009-9098-8     Document Type: Article

References (32)

1. Amento, B., Terveen, L., & Hill, W. (2000). Does "authority" mean quality? Predicting expert quality ratings of web documents. In SIGIR'00: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 296-303). New York, NY: ACM.
2. Anderson, W. J. (1991). Continuous-time Markov chains: An applications-oriented approach. New York: Springer-Verlag.
3. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. USA: Addison Wesley.
4. Berger, M. A. (1993). An introduction to probability and stochastic processes. New York: Springer-Verlag.
5. Bianchini, M., Gori, M., & Scarselli, F. (2005). Inside PageRank. ACM Transactions on Internet Technology, 5(1), 92-128.
6. Boldi, P., Santini, M., & Vigna, S. (2005). PageRank as a function of the damping factor. In WWW'05: Proceedings of the 14th international conference on world wide web (pp. 557-566). New York, NY: ACM.
7. Boyd, S., & Vendenberghe, L. (2003). Convex optimization. Cambridge: Cambridge University Press.
8. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1-7):107-117.
9. Eirinaki, M., & Vazirgiannis, M. (2005). Usage-based PageRank for web personalization. In ICDM'05: Proceedings of the fifth IEEE international conference on data mining (pp. 130-137). Washington, DC: IEEE Computer Society.
10. Golub, G. H., & Loan, C. F. V. (1996). Matrix computations, 3rd ed. Baltimore, MD: Johns Hopkins University Press.
11. Gyongyi, Z., & Garcia-Molina, H. (2005). Web spam taxonomy. In first international workshop on adversarial information retrieval on the web (AIRWeb).
12. Gyöngyi, Z., Garcia-Molina, H., & Pedersen, J. (2004). Combating web spam with TrustRank. In VLDB'04: Proceedings of the thirtieth international conference on very large data bases (pp. 576-587). VLDB Endowment.
13. Haveliwala, T. (1999). Efficient computation of PageRank. Technical report 1999-31, Stanford InfoLab.
14. Haveliwala, T., & Kamvar, S. (2003). The second eigenvalue of the google matrix. Technical report 2003-20, Stanford InfoLab.
15. Haveliwala, T., Kamvar, S., & Jeh, G. (2003, June). An analytical comparison of approaches to personalizing PageRank. Technical report 2003-35, Stanford InfoLab.
16. Haveliwala, T. H. (2002). Topic-sensitive PageRank. In WWW'02: Proceedings of the 11th international conference on world wide web (pp. 517-526). New York, NY: ACM.
17. Järvelin, K., & Kekäläinen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In SIGIR'00: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 41-48). New York, NY: ACM.
18. Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transcation on Information System 20(4), 422-446.
19. Kleinberg, J. M. (1998). Authoritative sources in a hyperlinked environment. In SODA'98: Proceedings of the ninth annual ACM-SIAM symposium on discrete algorithms (pp. 668-677). Philadelphia, PA: Society for Industrial and Applied Mathematics.
20. Langville, A. N., & Meyer, C. D. (2004). Deeper inside PageRank. Internet Mathematics, 1(3), 335-400.
21. Liu, Y., Gao, B., Liu, T.-Y., Zhang, Y., Ma, Z., He, S., & Li, H. (2008). BrowseRank: Letting web users vote for page importance. In SIGIR'08: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (pp. 451-458). New York, NY: ACM.
22. Lowry, R. (2000) Concepts and applications of inferential statistics. Vassar College. http://faculty.vassar.edu/lowry/webtext.htm.
23. McSherry, F. (2005). A uniform approach to accelerated PageRank computation. In WWW'05: Proceedings of the 14th international conference on world wide web (pp. 575-582). New York, NY: ACM.
24. Oztekin, B. U., Ertoz, L., Kumar, V., & Srivastava, J. (2003). Usage aware PageRank. Technical report 03-010, University of Minnesota.
25. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Technical report 1999-66, Stanford InfoLab. Previous number = SIDL-WP-1999-0120.
26. Rice, J. A. (1995). Mathematical statistics and data analysis, 2nd ed. USA: Duxbery Press.
27. Richardson, M., & Domingos, P. (2002). The intelligent surfer: Probabilistic combination of link and content information in PageRank. In advances in neural information processing systems 14 (pp. 1441-1448). Cambridge, MA: MIT Press.
28. Robertson, S. E. (1997). Overview of the Okapi projects. Journal of Documentatioin, 53(1), 3-7.
29. Sorenson, H. W. (1980). Parameter estimation: Principles and problems. USA: Marcel Dekker.
30. Stewart, W. J. (1994). Introduction to the numerical solution of Markov chains. Princeton, NJ: Princeton University Press.
31. Wang, Z. K., & Yang, X. Q. (1992). Birth and death processes and Markov chains. New York: Springer-Verlag.
32. White, R. W., Bilenko, M., & Cucerzan, S. (2007). Studying the use of popular destinations to enhance web search interaction. In SIGIR'07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 159-166). New York, NY: ACM.