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An Improved Usage-Based Ranking

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Book cover Advances in Web-Age Information Management (WAIM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2419))

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Abstract

A good ranking is critical to gain a positive searching experience. With usage data collected from past searching activities, it could be improved from current approaches which are largely based on text or link information. In this paper, we proposed a usage-based ranking algorithm. Basically, it calculates the rank score on time duration considering the propagated effect, which is an improvement on the simple selection frequency determined method. Besides, it also has some heuristics to further improve the accuracy of top-positioned results.

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References

  1. Balabanovic, Y. Shoham, “Fab: Content-based Collaborative Recommendation,” Communications of the ACM, 40(3), pp. 66–72, 1997.

    Article  Google Scholar 

  2. J. M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” IBM Research Report RJ 10076, 1997.

    Google Scholar 

  3. J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of ACM, 40(3), pp. 77–87, 1997.

    Article  Google Scholar 

  4. M. Marchiori, “The Quest for Correct Information on the Web: Hyper Search Engines,” Proceedings of the 6th World Wide Web Conference (WWW6), 1997.

    Google Scholar 

  5. B. Mobasher, R. Cooley, J. Srivastava, “Automatic Personalization Based on Web Usage Mining,” Technical Report TR99-010, Department of Computer Science, Depaul University, 1999.

    Google Scholar 

  6. M. Morita, Y. Shinoda, “Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval,” Proceedings of 17th ACM SIGIR, 1994.

    Google Scholar 

  7. L. Page, S. Brin, R. Motwani, T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web,” Stanford University working paper SIDL-WP-1997-0072, 1997.

    Google Scholar 

  8. M. Perkowitz, O. Etzioni, “Towards Adaptive Web Sites: Conceptual Framework and Case Study,” Proceedings of the 8th World Wide Web Conference (WWW8), 1999

    Google Scholar 

  9. P. Resnik, H. Varian, “Recommender Systems,” Communications of the ACM, 40(3), 1997.

    Google Scholar 

  10. G. Rodriguez-Mula, H. Garcia-Molina, A. Paepcke, “Collaborative Value Filtering on the Web,” Proceedings of the 7th World Wide Web Conference (WWW7), 1998.

    Google Scholar 

  11. G. Salton, M. J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., 1983.

    Google Scholar 

  12. T. W. Yan, M. Jacobsen, H. Garcia-Monila, U. Dayal, “From User Access Patterns to Dynamic Hypertext Linking,” Proceedings of the 5th World Wide Web Conference (WWW5), 1996.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, Lower Kent Ridge Road, Singapore, 119260

    Chen Ding & Chi-Hung Chi

  2. The Graduate School of Chinese Academy of Sciences, Beijing, P.R. China, 100039

    Chi-Hung Chi & Tiejian Luo

Authors
  1. Chen Ding
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  2. Chi-Hung Chi
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  3. Tiejian Luo
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Editor information

Editors and Affiliations

  1. Information School, Renmin University of China, Beijing, 100872, China

    Xiaofeng Meng

  2. Department of Computer Science, University of California, Santa Barbara, CA, 93106-5110, USA

    Jianwen Su  & Yujun Wang  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Ding, C., Chi, CH., Luo, T. (2002). An Improved Usage-Based Ranking. In: Meng, X., Su, J., Wang, Y. (eds) Advances in Web-Age Information Management. WAIM 2002. Lecture Notes in Computer Science, vol 2419. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45703-8_32

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  • DOI: https://doi.org/10.1007/3-540-45703-8_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44045-1

  • Online ISBN: 978-3-540-45703-9

  • eBook Packages: Springer Book Archive

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