Time Sensitive Ranking with Application to Publication Search



Link-based ranking has contributed significantly to the success of Web search. PageRank and HITS are the most well-known link-based ranking algorithms. These algorithms are motivated by the observation that a hyperlink from a page to another is an implicit conveyance of authority to the target page. However, these algorithms do not consider an important dimension of search, the temporal dimension. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. This project investigates the temporal aspect of search in the framework of PageRank with application to publication search. Existing remedies to PageRank are mostly heuristic approaches. This project proposes a principled method based on the stationary probability distribution of the Markov chain. The new algorithm, TS-Rank (for Time Sensitive Rank), generalizes PageRank. Methods are also presented to rank new papers that have few or no citations. The proposed methods are evaluated empirically; the results show the proposed methods are highly effective.


Citation Count Decay Parameter Source Evaluation Journal Evaluation Publication Search 



We thank KDD Cup 2003 organizers for making the publications and citation data available on the Web.


  1. 1.
    S. Abiteboul, M. Preda, and G. Cobena. Adaptive on-Line page importance computation. In WWW-2003.Google Scholar
  2. 2.
    D. Achlioptas, A. Fiat, A. Karlin, and F. McSherry. Web search via hub synthesis. In FOCS-2001.Google Scholar
  3. 3.
    A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, and S. Raghavan. Searching the Web. ACM Transactions on Internet Technology, 1(1): 2–43, 2001.CrossRefGoogle Scholar
  4. 4.
    R. Baeza-Yates, F. Saint-Jean, and C. Castillo. Web dynamics, age and page quality. In SPIRE-2002.Google Scholar
  5. 5.
    Z. Bar-Yossef, A. Z. Broder, R. Kumar, and A. Tomkins. Sic transit gloria telae: Towards an understanding of the Web’s decay, Pages 328–337, WWW-2004.Google Scholar
  6. 6.
    K. Bharat and A. Broder. A technique for measuring the relative size and overlap of public Web search engines. Computer Networks and ISDN Systems, 30: 379–388, 1998.CrossRefGoogle Scholar
  7. 7.
    K. Bharat and M. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. SIGIR-1998.Google Scholar
  8. 8.
    P. Boldi, M. Santini, and S. Vigna. PageRank as a function of the damping factor. Pages 557–566, WWW-2005.Google Scholar
  9. 9.
    A. Borodin, J. S. Rosenthal, G. O. Roberts, and P. Tsaparas, Finding authorities and hubs from link structures on the World Wide Web. WWW-2001.Google Scholar
  10. 10.
    A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph structure in the Web. WWW-2000.Google Scholar
  11. 11.
    S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30: 107–117, 1998.CrossRefGoogle Scholar
  12. 12.
    D. Cai, X. He, J-R. Wen, and W-Y. Ma: Block-level link analysis. SIGIR-2004.Google Scholar
  13. 13.
    S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, P. Raghavan, and S. Rajagopalan. Automatic resource compilation by analyzing hyperlink structure and associated text. WWW-1998.Google Scholar
  14. 14.
    S. Chakrabarti, M. van den Berg, and B. Dom. Focused crawling: A new approach to topic-specific Web resource discovery. WWW-1999.Google Scholar
  15. 15.
    Y.-Y. Chen, Q. Gan, and T. Suel. Local methods for estimating PageRank values. CIKM-2004.Google Scholar
  16. 16.
    J. Cho and S. Roy. Impact of web search engines on page popularity. WWW-2004.Google Scholar
  17. 17.
    J. Cho, S. Roy, and R. Adams. Page quality: In search of an unbiased Web ranking. SIGMOD-2005.Google Scholar
  18. 18.
    B. D. Davison. Toward a unification of text and link analysis. Poster abstract of SIGIR-2003.Google Scholar
  19. 19.
    P. Diaconis. Group Representation in Probability and Statistics. IMS Lecture Series 11, IMS, Hayward, CA, 1988.Google Scholar
  20. 20.
    M. Diligenti, M. Gori, and M. Maggini, Web page scoring systems for horizontal and vertical search. WWW-2002.Google Scholar
  21. 21.
    S. Dill, R. Kumar, K. S. McCurley, S. Rajagopalan, D. Sivakumar, and A. Tomkins. Self-similarity in the Web. VLDB-2001.Google Scholar
  22. 22.
    R. Fagin, R. Kumar, K. S. McCurley, J. Novak, D. Sivakumar, J. Tomlin, and D. Williamson. Searching the workplace Web. WWW-2003.Google Scholar
  23. 23.
    G. Flake, S. Lawrence, and C. L. Giles. Efficient identification of Web communities, Pages 150–160, KDD-2000. Google Scholar
  24. 24.
    C. L. Giles. CiteSeer: past, present, and future. AWIC-2004.Google Scholar
  25. 25.
    T. Haveliwala. Extrapolation methods for accelerating PageRank computations, WWW-2003.Google Scholar
  26. 26.
    R. Jin and S. T. Dumais. Probabilistic combination of content and links. SIGIR-2001.Google Scholar
  27. 27.
    S. D. Kamar, T. Haveliwala, C. D. Manning, and G. H. Golub, Extrapolation methods for accelerating PageRank computations. WWW-2003.Google Scholar
  28. 28.
    J. Kleinberg. Authoritative sources in a hyperlinked environment. ACM-SIAM Symposium on Discrete Algorithms, 1998.Google Scholar
  29. 29.
    J. Kleinberg, S. R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. The Web as a graph: Measurements, models, and methods. International Conference on Combinatorics and Computing, 1999.Google Scholar
  30. 30.
    R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Social networks: From the Web to knowledge management. Web Intelligence, Pages 367–379, January 2003.Google Scholar
  31. 31.
    S. Lawrence, K. Bollacker, and C. L. Giles. Indexing and retrieval of scientific literature. CIKM-1999.Google Scholar
  32. 32.
    R. Lempel and S. Moran, The stochastic approach for link-structure analysis (SALSA) and the TKC effect, WWW-2000.Google Scholar
  33. 33.
    F. McSherry. A uniform approach to accelerated PageRank computation. WWW-2005. Google Scholar
  34. 34.
    Z. Nie Y. Zhang, J-R. Wen, and W-Y Ma. Object level ranking: Bringing order to Web objects. WWW-2005.Google Scholar
  35. 35.
    A. Ntoulas, J. Cho, and C. Olston. What’s new on the Web? the evolution of the Web from a search engine perspective. WWW-2004.Google Scholar
  36. 36.
    S. Pandey, S. Roy, C. Olston, J. Cho, and S. Chakrabarti. Shuffling a stacked deck: The case for partially randomized ranking of search engine results. VLDB-2005.Google Scholar
  37. 37.
    W. Steward. Introduction to the Numerical Solution of Markov Chains. Princeton University Press, Princeton, NJ, 1994.Google Scholar
  38. 38.
    J. Tomlin. A new paradigm for ranking pages on the World Wide Web. WWW-2003.Google Scholar
  39. 39.
    P. S. Yu, X. Li, and B. Liu. Adding the temporal dimension to search—a case study in publication search, WI-2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Microsoft Corporation One Microsoft WayRedmondUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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