A Review on Personalized Web Search Using Server Side Cache Approach

Authors

  • Kiran Kamble  M-Tech Scholar, Department of Computer Science and Engineering, Tulsiramji Gaikwad-Patil College of Engineering & Technology, Nagpur, Maharashtra, India.
  • Prof.Rajesh Babu  Assistant Professor Department of Computer Science and Technology Tulsiramji Gaikwad-Patil College of Engineering & Technology, Nagpur, Maharashtra, India.
  • Prof. Jayanth Adhikari  Assistant Professor Department of Computer Science and Technology Tulsiramji Gaikwad-Patil College of Engineering & Technology, Nagpur, Maharashtra, India.

Keywords:

Cyber Physical Social Systems, PageRank algorithm, Personalized Web search, Web Logs .

Abstract

Web page recommendation is the technique of web site customization to fulfill the needs of every particular user or group of users. The web has become largest world of knowledge. So it is more crucial task of the webmasters to manage the contents of the particular websites to gather the requirements of the web users. The web page recommendation systems most part based on the exploitation of the patterns of the sites visitors. Domain ontologys provide shared and regular understanding of a particular domain. Existing system uses preorder linked WAP-tree mining (PLWAP Mine) algorithm that helps web recommendation system to recommend the interested pages but it has some drawbacks, it requiremore execution time and memory and also it does not work on updated dataset. To overcome these drawbacks of the system utilizes PREWAP algorithm. The PREWAP algorithm recommends the interested results to web user within less time and also it requires less memory compare to PLWAP Mine algorithm which improves the efficiency of web page recommendation system. In work, various models are presented; the first model is Web Usage Mining which uses the web logs. The second model also utilizes web logs to represent the domainknowledge, here the domain ontology is used to solve the new page problem. Likewise the prediction model, which is a network of domain terms, which isbased on the frequently viewed web-pages and represents the integrated web usage. The recommendation results have been successfully verified based on the results which are acquired from a proposed and existing web usage mining(WUM) technique.

References

  1. Christie I. EZEIFE 1, Monwar Mostafa, ”a PLWAP-based algorithm for mining frequent sequential stream patterns”, Springer.
  2. Amit Bose, Kalyan Beemanapalli, Jaideep Srivastava, Sigal Sahar, "Incorporating Concept Hierarchies into Usage Mining Based Recommendations", 2006 ACM 1-59593-444-8.
  3. Dale Dzemydiene, Lina Tankeleviciene, ”on the development of domain ontology for distance learning course”, ISBN 978-9955-28-283-9.
  4. Thi Thanh Sang Nguyen* and Hai Yan Lu, ”Investigation of sequential pattern mining techniques for web recommendation”, Int. J. Information and Decision Sciences, Vol. 4, No. 4, 2012.
  5. Sebastian A. R?os Juan D. Velasquez, ”Semantic Web Usage Mining by a Conceptbased Approach for Off-lineWeb Site Enhancements” IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2008.
  6. Borges, J. Levene, M. 2004, A Dynamic Clustering-Based Markov Model for Web Usage Mining,Available online at http://xxx.arxiv.org/abs/cs.IR/0406032.
  7. Liu, B., Mobasher, B. Nasraoui, O. 2011, ’Web Usage Mining’, in B. Liu (ed.), Web Data Mining:Exploring Hyperlinks, Contents, and Usage Data, Springer-Verlag Berlin Heidelberg, pp.527-603.
  8. Liu, B., Mobasher, B. Nasraoui, O. 2011, ’Web Usage Mining’, in B. Liu (ed.), Web Data Mining:Exploring Hyperlinks, Contents, and Usage Data, Springer-Verlag Berlin Heidelberg, pp.527-603.
  9. J. Rennie and A. Mccallum, ``Using reinforcement learning to spider the Web ef_ciently,'' in Proc. 6th Int. Conf. Mach. Learn., 1999, pp. 335_343.
  10. Peng, Xueping, ZhendongNiu, Sheng Huang, and YuminZhao."Personalized Web Search Using Clickthrough Data and Web Page Rating."Journal of Computers 7, no. 10 (2012): 2578-2584
  11. E. Volokh. Personalization and privacy. Communications of the ACM, 43(8):84–88, 2000.
  12. Sugiyama, K., Hatano, K. and Yoshikawa, M. (2004). Adaptive Web search based on user profile constructed without any effort from user. In Proceedings of WWW ‘04, 675-684.
  13. M Speretta and S Gauch , "Personalized Search Based on User Search Histories", Proceeding Of International Conference on Web
  14. Intelligence, pp. 622-628,2005.
  15. C Liang, "User Profile for Personalized Web Search", International Conference on Fuzzy Systems And Knowledge Discovery, pp. 1847- 1850,2011 .
  16. N. Matthijs and F. Radlinski.(2011) “Personalizing Web search using long term browsing history “. In Proceedings of the ACMWSDM Conference on Web Search and Data Mining, pp. 25–34.
  17. E. A. Lee, “Cyber Physical Systems: Design Challenges,” in11th IEEEInternational Symposium on Object Oriented Real-Time DistributedComputing (ISORC 2008).Orlando, FL: IEEE, May 2008.
  18. R. R. Rajkumar, I. Lee, L. Sha, and J. Stankovic, “Cyber-physicalSystems: The Next Computing Revolution,” in47th Design AutomationConference (DAC 2010). Anaheim, CA: ACM, Jun. 2010, pp. 731–736.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-06-30

Issue

Volume 6, Issue 3, May-June-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kiran Kamble, Prof.Rajesh Babu, Prof. Jayanth Adhikari, " A Review on Personalized Web Search Using Server Side Cache Approach, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 3, pp.17-23, May-June-2019.