Advertisement

Wireless Personal Communications

, Volume 87, Issue 2, pp 499–512 | Cite as

Role of Agent Technology in Web Usage Mining: Homomorphic Encryption Based Recommendation for E-commerce Applications

  • S. Sobitha AhilaEmail author
  • K. L. Shunmuganathan
Article

Abstract

Recently keen interests shown in studying the goal behind a user’s Web query, so that this goal can be used to improve the quality of a search engine’s results in turn improves the popularity of web pages. Advertisement fees can be decided based on this factor. Personalization is now becoming common term for improving E-commerce services and attract more users. Today’s recommender system provides suggestion for specific items but drawback that service provider can increase the ratings of specific product and unnecessarily popularity increases. Traditional data protection mechanisms focus on access control and secure transmission, which provide security only against malicious third parties, but not the service provider. This leads to misguiding the users while purchasing some products, so privacy is violated. Many different approaches including web usage mining have been applied to the basic problem of developing accurate and efficient recommendation systems. Online business transactions and the success of E-commerce depend greatly on the effective design of a product recommender mechanism. Our proposal is founded on homomorphic encryption, which is used to obscure the private rating information of the customers from the service provider. While the user’s privacy is respected by the service provider, by generating recommendations using encrypted customer ratings, the service provider’s commercially valuable item–item similarities are protected against curious entities, in turn. Agent based Web mining has advantages of both Web mining and Agent: it can mine data efficiently and intelligently, so it is becoming more and more important in modern E-business.

Keywords

Web mining Agent Recommender system E-commerce 

References

  1. 1.
    AHamo, Y. & Aljawaherry, M. A. (2012). Constructing a collaborative multi-agents system tool for realtime system requirements. International Journal of Computer Science (IJCSI), 9(4), 134–140.Google Scholar
  2. 2.
    Cho, Y., & Kim, J. (2004). Application of Web usage mining and product taxonomy to collaborative recommendations in ecommerce. Expert Systems with Applications, 26(2), 233–246.CrossRefGoogle Scholar
  3. 3.
    Cho, Y., Kim, J., & Kim, S. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(3), 329–342.CrossRefGoogle Scholar
  4. 4.
    Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1, 5–32.CrossRefGoogle Scholar
  5. 5.
    Devi, B., Devi, Y., Rani, B., & Rao, R. (2012). Design and implementation of web usage mining intelligent system in the field of e-commerce. In International conference on communication technology and system design procedia engineering (Vol. 30, pp. 20–27).Google Scholar
  6. 6.
    Domenech, J. M., & Lorenzo, J. (2007). A tool for web usage mining. In 8th international conference on intelligent data engineering and automated learning, 2007.Google Scholar
  7. 7.
    Erkin, Z., Beye, M., Veugen, T., & Lagendijk, R. L. (2010). Privacy enhanced recommender system. In Thirty-first symposium on information theory in the Benelux (pp. 35–42), Rotterdam.Google Scholar
  8. 8.
    Evfimievski, A., & Grandison, T. (2009). Privacy-preserving data mining. San Jose: IBM Almaden Research Center.CrossRefGoogle Scholar
  9. 9.
    Foner, L. (1999). Political artifacts and personal privacy: The yenta multi-agent distributed matchmaking system. PhD thesis, MIT.Google Scholar
  10. 10.
    Fürnkranz, J. (2005). Web mining. In O. Maimon & L. Rokach (Eds.), The data mining and knowledge discovery handbook (pp. 899–920). Berlin: Springer.CrossRefGoogle Scholar
  11. 11.
    Gahi, Y., Guennoun, M., & El-Khatib, K. (2011). A secure database system using homomorphic encryption schemes. In The third international conference on advances in databases, knowledge, and data applications (DBKDA) (pp. 54–58).Google Scholar
  12. 12.
    Huang, Y. M., Kuo, Y. H., Chen, J. N., & Jeng, Y. L. (2006). NP-miner: A real-time recommendation algorithm by using web usage mining. Knowledge-Based Systems, 19(4), 272–286.CrossRefGoogle Scholar
  13. 13.
    Jennings, N. R. (2000). On agent-based software engineering. Artificial Intelligence, 117(2), 277–296.CrossRefzbMATHGoogle Scholar
  14. 14.
    Jiao, J., You, X., & Kumar, A. (2006). An agent-based framework for collaborative negotiation in the global manufacturing supply chain network. Robotics and Computer-Integrated Manufacturing, 22(3), 239–255.CrossRefGoogle Scholar
  15. 15.
    Khosravi, M., & Tarokh, M. J. (2010). Dynamic mining of users interest navigation patterns using naive bayesian method. In Intelligent computer communication and processing (ICCP). IEEE (pp. 119–122).Google Scholar
  16. 16.
    Kolari, P., & Joshi, A. (2004). Web mining: Research and practice. Computing in Science & Engineering, 6(4), 49–53.CrossRefGoogle Scholar
  17. 17.
    Lindell, Y., & Pinkas, B. (2000). Privacy preserving data mining. In Lecture notes in computer science. Proceedings of advances in cryptology: Crypto’ 2000 (Vol. 1880, pp. 20–24). Springer, Berlin.Google Scholar
  18. 18.
    Link, H., Saia, J., Lane, T., & LaViolette, R. A. (2005). The impact of social networks on multi-agent recommender systems. In Proceedings of the workshop on cooperative multi-agent learning (ECML/PKDD’05).Google Scholar
  19. 19.
    Marivate, V. N., Ssali, G., & Marwala, T. (2008). An intelligent multi-agent recommender system for human capacity building (pp. 909–915).Google Scholar
  20. 20.
    Paydar, S., & Kahani, M. (2011). An agent-based framework for automated testing of web-based systems. Journal of Software Engineering and Applications, 4, 86–94.Google Scholar
  21. 21.
    Qingning, H., Hong, Z., & Greenwood, S. (2003). A multi-agent software engineering environment for testing web-based applications (pp. 210–215).Google Scholar
  22. 22.
    Varnagar, C. R., Madhak, N. N., Kodinariya, T. M. & Rathod, J. N. (2013). Web usage mining: A review on process, methods and techniques. In International conference on information communication and embedded systems (ICICES) (pp. 40–46). IEEE.Google Scholar
  23. 23.
    Yao, A. C. (1982). Protocols for secure computations. IEEE Foundations of Computer Science. In SFCS ‘08. 23rd annual symposium (pp. 160–164). doi: 10.1109/SFCS.1982.38.

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringEaswari Engineering CollegeChennaiIndia
  2. 2.Department of Computer Science and EngineeringR.M.K Engineering CollegeChennaiIndia

Personalised recommendations