A Layered Approach to Revisitation Prediction

  • George Papadakis
  • Ricardo Kawase
  • Eelco Herder
  • Claudia Niederée
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6757)

Abstract

Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users’ navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD Conference, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: ICDE, pp. 3–14 (1995)Google Scholar
  3. 3.
    Awad, M., Khan, L., Thuraisingham, B.M.: Predicting www surfing using multiple evidence combination. VLDB J. 17(3), 401–417 (2008)CrossRefGoogle Scholar
  4. 4.
    Cockburn, A., McKenzie, B.J.: What do web users do? an empirical analysis of web use. Int. J. Hum.-Comput. Stud. 54(6), 903–922 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Cormode, G., Shkapenyuk, V., Srivastava, D., Xu, B.: Forward decay: A practical time decay model for streaming systems. In: ICDE, pp. 138–149 (2009)Google Scholar
  6. 6.
    Deshpande, M., Karypis, G.: Selective markov models for predicting web page accesses. ACM Trans. Internet Techn. 4(2), 163–184 (2004)CrossRefGoogle Scholar
  7. 7.
    Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: IUI, pp. 106–112 (2000)Google Scholar
  8. 8.
    Géry, M., Haddad, M.H.: Evaluation of web usage mining approaches for user’s next request prediction. In: WIDM, pp. 74–81 (2003)Google Scholar
  9. 9.
    Hawking, D., Craswell, N., Bailey, P., Griffiths, K.: Measuring search engine quality. Inf. Retr. 4(1), 33–59 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Herder, E.: Characterizations of user web revisit behavior. In: LWA, pp. 32–37 (2005)Google Scholar
  11. 11.
    Kawase, R., Papadakis, G., Herder, E., Nejdl, W.: The impact of bookmarks and annotations on refinding information. In: HT, pp. 29–34 (2010)Google Scholar
  12. 12.
    Kazienko, P.: Mining indirect association rules for web recommendation. Applied Mathematics and Computer Science 19(1), 165–186 (2009)MATHGoogle Scholar
  13. 13.
    Koychev, I., Schwab, I.: Adaptation to drifting user’s interests. In: ECML Workshop: Machine Learning in New Information Age, Citeseer, pp. 39–46 (2000)Google Scholar
  14. 14.
    Papadakis, G., Niederee, C., Nejdl, W.: Decay-based ranking for social application content. In: WEBIST, pp. 276–282 (2010)Google Scholar
  15. 15.
    Parameswaran, A.G., Koutrika, G., Bercovitz, B., Garcia-Molina, H.: Recsplorer: recommendation algorithms based on precedence mining. In: SIGMOD, pp. 87–98 (2010)Google Scholar
  16. 16.
    Sandvig, J.J., Mobasher, B., Burke, R.: Robustness of collaborative recommendation based on association rule mining. In: RecSys, pp. 105–112 (2007)Google Scholar
  17. 17.
    Tauscher, L., Greenberg, S.: How people revisit web pages: empirical findings and implications for the design of history systems. Int. J. Hum.-Comput. Stud. 47(1), 97–137 (1997)CrossRefGoogle Scholar
  18. 18.
    Teevan, J., Adar, E., Jones, R., Potts, M.A.S.: Information re-retrieval: repeat queries in yahoo’s logs. In: SIGIR, pp. 151–158 (2007)Google Scholar
  19. 19.
    Tyler, S.K., Teevan, J.: Large scale query log analysis of re-finding. In: WSDM, pp. 191–200 (2010)Google Scholar
  20. 20.
    Yao, Y., Shi, L., Wang, Z.: A markov prediction model based on page hierarchical clustering. Int. J. Distrib. Sen. Netw. 5(1), 89–89 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • George Papadakis
    • 1
    • 2
  • Ricardo Kawase
    • 2
  • Eelco Herder
    • 2
  • Claudia Niederée
    • 2
  1. 1.ICCS, National Technical Unversity of AthensGreece
  2. 2.L3S Research CenterLeibniz University of HanoverGermany

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