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A Web Content Recommendation Method Based on Data Provenance Tracing and Forecasting

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 236)

Abstract

How to choose an appropriate releasing strategy for site content, and which one caters to user’s habits, have become the main challenges. This article provides a provenance-aware model to design the content of the website. Based on the user’s browsing history data, it constructs timed automaton that can trace the provenance of the data to find what the user may be interested in, and it establishes a Markov chain model to determine the content of the link relationship. Experiments show this model not only meets the dynamic needs of users when they browses the site, but also gives certain options to the administrator of site content. It provides recommending result efficiently and should have a bright application prospect.

Keywords

Content recommendation Data provenance Timed automation Markov chain 

Notes

Acknowledgments

This work is supported in part by China Postdoctoral Science Foundation (2012M511227), Jiangsu Province Postdoctoral Science Research Fund (1101073C), National Natural Science Foundation of China (61170035), Natural Science Foundation of Jiangsu (BK2011022, BK2011702).

References

  1. 1.
    Wang, J., Tang, X.: Personalized recommendation algorithm research based on content in social network. Applica. Res. Comput. 24(8), 1248–1250 (2011)Google Scholar
  2. 2.
    Woodruff, A., Stonebraker, M.: Supporting fine-grained data lineage in a database visualization environment. In: Proceedings of the International Conference on Data Engineering (IEEE ICDE), pp. 91–102 (1997)Google Scholar
  3. 3.
    Liu, X., Wan, C.: Research on data provenance an overview. Sci. Mosaic 1, 47–52 (2005)Google Scholar
  4. 4.
    Kiran-Kumar, M., Uri, B., David, A.H., Peter M., Diana M., Daniel M., Margo S., Robin S.: Layering in Provenance Systems. USENIX Annual technical conference (2009)Google Scholar
  5. 5.
    Zoé, L, Christophe, L., Spyro, M.: Storing Scientific Workflows in a Database. ACM (2009)Google Scholar
  6. 6.
    Geoffrey, R.G., David R. S.: Probability and Random Processes. Oxford University Press, USA; 3 edition (2001)Google Scholar
  7. 7.
    Deng, M.: Research on the top three places in men’s modern pentathlon of olympic using gray markov chain prediction model. Mathemat. Pract. Theory 41(2), 134–137 (2011)Google Scholar
  8. 8.
    Herrlocker, J., Konstan, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of EconomicsNanjing UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina

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