Privacy Based Personalized Web Search Engine

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-34 Number-3
Year of Publication : 2016
Authors : R.Monika, V.Pavithra, D.PriyaDharshini, N.Vaishnavi, S.Gowthami
  10.14445/22312803/IJCTT-V34P121

MLA

R.Monika, V.Pavithra, D.PriyaDharshini, N.Vaishnavi, S.Gowthami "Privacy Based Personalized Web Search Engine". International Journal of Computer Trends and Technology (IJCTT) V34(3):122-124, April 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
With the growth of (WWW) World Wide Web, Search engines have more contribution in giving information from the web to the user. They help in searching information on the web robust and easy. But there is still room for improvement. Existing web search engines do not consider particular needs of user and serve each user equally. For this ambiguous query, a number of documents on distinct topics are returned by search engines. Hence it becomes difficult for the user to get the specified content. Moreover it also takes more time in searching a pertinent content. Personalized Web Search Engine is considered as a promising solution to handle these problems, since different search results can be provided depending upon the choice and information needs of users. It exploits user information and search context to learning in which sense a query refer. In order to perform Personalized Web search it is important to model User's interest. User profiles are constructed to model user's need based on his/her web usage data .This Enhanced User Profile will help the user to retrieve concentrated information .This paper proposes architecture for constructing user profile and enhances the user profile using background knowledge. It can be used for suggesting good web pages to the user based on his search query and background knowledge.

References
[1] C.-C. Ana. Improving Methods for Single-label Text Categorization. PdD Thesis, Instituto Superior Tecnico, Universidade Tecnica de Lisboa, 2007.
[2] H. Attias. A variational bayesian framework for graphical models. In Advances in Neural Information Processing Systems (NIPS), pages 209–215, 2000.
[3] D. Blei and M. Jordan. Modeling annotated data. In Proceedings of the 26th Annual International ACM SIGIR conference on Research and Development in Informaion Retrieval, pages 127–134. ACM, 2003.
[4] D. M. Blei, A. Ng, and M. I. Jordan. Latent Dirichlet Allocation. In Advances in Neural Information Processing Systems (NIPS), pages 601–608, 2002.
[5] D. M. Blei, A. Ng, and M. I. Jordan. Latent dirichlet allocation. The Journal of Machine Learning Research, 3:993– 1022, 2003.
[6] D. M. Blei and J. Lafferty. Correlated topic models. In Advances in Neural Information Processing Systems (NIPS), pages 147–154, 2006.
[7] D. M. Blei and J. McAuliffe. Supervised topic models. In Advances in Neural Information Processing Systems (NIPS), pages 121–128, 2008.

Keywords
search engine, personalized web search, background knowledge.