Email this article A Framework For Protecting The Privacy In Web Search
Abstract
Customized web pursuit could be customizing so as to promise because of enhance hunt quality indexed lists for people with individual information objectives. Then again, client's region unit uncomfortable with uncovering non-open inclination data to make a go at looking motors. On the inverse hand, security isn't total, and occasionally will be traded off if there's an addition in commission or benefit to the client. Therefore, a parity ought to be stricken between hunt quality and security insurance. This paper shows a climbable way for clients to mechanically assemble made client profiles. These profiles abridge a client's advantage into a stratified association in venture with particular hobbies. 2 parameters for determining protection needs range unit wanted to help the client to settle on the substance and level of point of interest of the profile data that is presented to the PC program. Trials demonstrated that the client profile enhanced hunt quality contrasted with plain MSN rankings. a great deal of altogether, results checked our theory that a noteworthy change on hunt quality will be accomplished by exclusively sharing some larger amount client profile information, that is most likely less touchy than intricate individual data.
Keywords
References
Lidan Shou, He Bai, Ke Chen, and Gang Chen “Supporting Privacy Protection in Personalized Web Search,†IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 2, Feb 2014.
Z. Dou, R. Song, and J.-R. Wen, “A Large-Scale Evaluation and Analysis of Personalized Search Strategies,†Proc. Int’l Conf. World Wide Web (WWW), pp. 581-590, 2007.
J. Teevan, S.T. Dumais, and E. Horvitz, “Personalizing Search via Automated Analysis of Interests and Activities,†Proc. 28th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), pp. 449-456, 2005.
M. Spertta and S. Gach, “Personalizing Search Based on User Search Histories,†Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI), 2005.
B. Tan, X. Shen, and C. Zhai, “Mining Long-Term Search History to Improve Search Accuracy,†Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), 2006.
K. Sugiyama, K. Hatano, and M. Yoshikawa, “Adaptive Web Search Based on User Profile Constructed without any Effort from Users,†Proc. 13th Int’l Conf. World Wide Web (WWW), 2004.
X. Shen, B. Tan, and C. Zhai, “Implicit User Modeling for Personalized Search,†Proc. 14th ACM Int’l Conf. Information and Knowledge Management (CIKM), 2005.
X. Shen, B. Tan, and C. Zhai, “Context-Sensitive Information Retrieval Using Implicit Feedback,†Proc. 28th Ann. Int’l ACM SIGIR Conf. Research and Development Information Retrieval (SIGIR), 2005.
F. Qiu and J. Cho, “Automatic Identification of User Interest for Personalized Search,†Proc. 15th Int’l Conf. World Wide Web (WWW), pp. 727-736, 2006.
J. Pitkow, H. Schu¨ tze, T. Cass, R. Cooley, D. Turnbull, A. Edmonds, E. Adar, and T. Breuel, “Personalized Search,â€.
Xueming Qian, He Feng, Guoshuai Zhao, and Tao Mei, “Personalized Recommendation Combining User Interest and Social Circleâ€,IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 7, July 2014.
Zheng Lu, Hongyuan Zha, Xiaokang Yang, Weiyao Lin, and Zhaohui Zheng, “A New Algorithm for Inferring User Search Goals with Feedback Sessions†, IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 3, March 2013
A. Krause and E. Horvitz, “A Utility- Theoretic Approach to Privacy in Online Services,†J. Artificial Intelligence Research,vol. 39, pp. 633-662, 2010.
P.Anick. Using terminological feed back for Web search refinement: a log-based study. In Proc. of the 13th International World Wide Web Conference (WWW), New York, New York, May 2004.
K.R. McKeown, N. Elhadad, and V. Hatzivassiloglou. Leveraging a common representation for personalized search and summarization in a medical digital library. In Proc. of International Conference on Digital Library, 2003
A. Kritikopoulos, and M. Sideri. The compass Filter: Search engine result personalization using web communities. In Proc. of Intelligent Techniques in Web Personalization (ITWP), 2003.
B. Fung, K. Wang and M. Ester. Hierarchical document clustering using frequent itemsets. In Proc. Of SIAM International Conference on Data Mining, San Francisco, May 2003.
K. Wang, C. Xu, B. Ling, "Clustering transactions using large items", In Proc. of the 8th Conference on Information and Knowledge Management (CIKM), Kansas City, November, 1999.
J. Sun, H. Zeng, H. Liu, Y. Lu, and Z. Chen. CubeSVD: A Novel Approach to Personalized Web Search. In Proc. of the 14th International World Wide Web Conference (WWW), Chiba, Japan, May 2005.
Refbacks
- There are currently no refbacks.
Copyright © 2013, All rights reserved.| ijseat.com
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.
Â