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An Integrated Page Ranking Algorithm for Personalized Web Search
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 11 |
| Year of Publication: 2011 |
| Authors: J.Jayanthi, Dr.K.S.Jayakumar |
10.5120/1732-2350
|
J.Jayanthi, Dr.K.S.Jayakumar . An Integrated Page Ranking Algorithm for Personalized Web Search. International Journal of Computer Applications. 12, 11 ( January 2011), 1-5. DOI=10.5120/1732-2350
Abstract
Today search engines constitute the most powerful tools for organizing and extracting information from the Web. However, it is not uncommon that even the most renowned search engines return result sets including many pages that are definitely useless for the user. This is mainly due to the fact that the very basic relevance criterions underlying their information retrieval strategies rely on the presence of query keywords within the returned pages. Web Search Personalization is a process of customizing the Web search experience of individual users. The goal of such personalization may range from simply providing the user with a more satisfied results by relevant information. Such a system must be able to deduce the information needs of the user. It is worth observing that statistical algorithms are applied to “tune” the result and, more importantly, approaches based on the concept of relevance feedback are used in order to maximize the satisfaction of user’s needs. Nevertheless, in some cases, this is not sufficient. In this paper search results are ranked based on user preferences in content and link. The preference of content and link is integrated in order to rank the results.
References
- Boanerges Aleman-Meza, Chris Halaschek, I. Budak Arpinar and Amit Sheth “Context-Aware Semantic Association Ranking” 2003.
- Fabrizio Lamberti, Andrea Sanna, and Claudio Demartini “A Relation-Based Page Rank Algorithm for Semantic Web Search Engines” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 1, January 2009.
- W. P. Lee and M. H. Su, “Personalizing information services on wired and wireless networks,” in EEE. IEEE Computer Society, 2004, pp.263–266.
- S. Gauch, M. Speretta, A. Chandramouli, and A. Micarelli, “User profiles for personalized information access,” in The Adaptive Web
- Lecture Notes in Computer Science, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds., vol. 4321. Springer, 2007, pp. 54–89.
- G. S. B. Nelson, “Avoiding overload:personalizing web content through security, eintelligence and data mining,” in Proc. of the SouthEast SAS Users Group Conference, New Orleans, Louisiana, August 19-22 2001.
- P. Brusilovsky and C. Tasso, “Preface to special issue on user modeling for web information retrieval,” User Model. User-Adapt. Interact.,vol. 14, no. 2-3, pp. 147–157, 2004.
- A. McCallum, “Information extraction: distilling structured data fro unstructured text,” ACM Queue, vol. 3, no. 9, pp. 48–57, 2005.
- B. J. Jansen, D. L. Booth, and A. Spink, “Determining the informational, navigational, and transactional intent of web queries,” Inf. Process. Manage. vol. 44, no. 3, pp. 1251–1266, 2008.
- Hang Cui; Ji-Rong Wen; Jian-Yun Nie; Wei-YingMa, “Query expansion by mining user logs,” IEEETransactions on Knowledge and Data Engineering, page(s): 829- 839, July-Aug. 2003.
- Taher Haveliwala, Aristides Gionis , Dan Klein, and Piotr Indyk. “Evaluating strategies for similarity search on the web,” In Proceedings of the Eleventh International World Wide Web Conference, May 2002.
- Philippe Poinçot, Soizick Lesteven, and Fionn Murtagh. “Comparison of two document similarity search engines,” Library and Information Services in Astronomy III, ASP Conference Series, Vol. 153, 1998
- Tomkins.“Mining the link structure of the world wide web,” IEEE Computer, 32(8): 60-67, 1999.
- H. Ahonen, O. Heinonen, M. Klemettinen, and A.Verkamo. “Finding co-occurring text phrases by combining sequence and frequent set discovery.” In R.Feldman, editor, Proceedings of 16th International Joint Conference on Artificial Intelligence IJCAI-99Workshop on Text Mining: Fiundations, Techniques and Applications, page 1-9, 1999
- Fang Liu; Yu, C.; Weiywe Meng. “Personalized web search for improving retrieval effectiveness,” IEEE Transaction on knowledge and data engineering,Volume: 16, Issue: 1, page 28-40, Jan. 2004.
- W. Fan, M.D. Gordon, P. Pathak, Personalization of search engine services for effective retrieval and knowledgemanagement, in the Proceedings of the 2000 International Conference on Information Systems(ICIS), 2000, Brisbane, Australia.
- Haixuan Yang, Irwin King, and Michael R. Lyu “DiffusionRank: A Possible Penicillin for Web Spamming” Proceedings SIGIR ACM 2007.
- Kemafor Anyanwu, Angela Maduko and Amit Sheth “SemRank: Ranking Complex Relationship Search Results on the Semantic Web” ACM, 2005.
- Li Ding, Rong Pan, Tim Finin, Anupam Joshi, Yun Peng, and Pranam Kolari “Finding and Ranking Knowledge on the Semantic Web” preprint from the Proceedings of the 4th International Semantic Web Conference, Galway IE, Springer-Verlag, November 2005.
- Oren Kurland, Lillian Lee “PageRank without Hyperlinks: Structural ReRanking using Links Induced by Language Models” ACM 2005.
- D. Mladenic. “ Text -learning and related intelligent agents,” IEEE Intelligent Systems , 14 (4): 44-54, 1999.
- E.M. Voorhees and D. Harman, eds., “Common Evaluation Measures,” Proc. Text REtrieval Conf.(TREC-10), p.A-14 ,2001.
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