Abstract
The Web is a vast and dynamic medium for accessing a great variety of information stored in various locations in the entire world. In recent days, the users are relying on Web and various Web search tools for retrieving the needed information. In order to easily get the exact information from the dynamic Web environment, search engines come into picture and they keep on developing. The currently existing search engines do not fulfill user’s prerequisites, because of its traditional indexing and other techniques. Since users have their particular goal and intensions during their search process, it is fuzzy and is very tedious to predict. In order to achieve the user’s specific goal and needs, personalized search mechanism can be introduced. Personalized Web search is a process of gathering information based on the user’s interest which can be guessed from their actions performed during search. The main objective of this paper is to provide an overview of Fuzzy Neural Networks based approaches and hence an outline to how such approaches may be adopted for personalized Web search
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References
Youssif, A.A., Darwish, A.A., Roshdy, A.: Development of Information Agent Reranking By Using Weights Measurement. International Journal of Computer Science & Security (IJCSS) 4(2), 256–264 (2009)
Rauber, A., Kaiser, M., Wachter, B.: Ethical Issues in Web Archive Creation and Usage-Towards a Research Agenda. Univ. of Brighton, Brighton (2008)
Alhenshiri, A., Watters, C., Shepherd, M.: User Behaviour during Web Search as Part of Information Gathering. IEEE (2011); ISSN:1530-1605/11 $26.00
Lucchesez, C., Orlandoy, S., Peregoz, R., Silvestriz, F., Tolomeizy, G.: Detecting Task-based Query Sessions Using Collaborative Knowledge. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE (2010); ISSN:978-0-7695-4191-4/10 $26.00
Chang, C.C., Chen, P.-L., Chiu, F.-R., Chen, Y.-K.: Application of neural networks and Kano’s method to content recommendation in Web personalization. Journal of Expert systems with Applications 36, 5310–5316 (2009)
Chai, C., Li, B.: A Novel Association Rules Method Based on Genetic Algorithm and Fuzzy Set Strategy for Web Mining. Journal of Computers 5(9) (September 2010)
Miao, C., Yang, Q., Fang, H., Goh, A.: Fuzzy Cognitive Agents for Personalized Recommendation. In: Proceedings of the 3rd International Conference on Web Information Systems Engineering (WISE 2002). IEEE (2009); ISSN: 0-7695-1766-8/02 $17.00
Mukhopadhyay, D., Biswas, P., Kim, Y.-C.: A Syntactic Classification based Web Page Ranking Algorithm. In: 6th International Workshop on MSPT Proceedings MSPT (2006)
Minkov, E., Cohen, W.W.: Improving Graph-Walk Based Similarity with Reranking: Case Studies for Personal Information Management. ACM Transactions on Computational Logic 2(3), 111–160 (2001)
Paulovich, F.V., Pinho, R., Botha, C.P., Heijs, A., Minghim, R.: PEx-WEB: Content-based visualization of Web search results. In: 12th International Conference Information Visualisation. IEEE (2008); ISSN:1550-6037/08 $25.00
Castellano, G., Fanelli, A.M., Torsello, M.A.: Mining usage profiles from access data using fuzzy clustering. In: Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, September 22-24 (2006)
Castellano, G., Fanelli, A.M., Torsello, M.A.: NEWER:A system for NEuro-fuzzy Web Recommendation. Journal of Applied Soft Computing 11, 739–806 (2011)
Gauch, S., Speretta, M., et al.: User Profiles for Personalized Information Access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)
Kumar, H., Park, S., Kang, S.: A Personalized URL Re-ranking Methodology Using User’s Browsing Behavior. In: Nguyen, N.T., Jo, G.-S., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2008. LNCS (LNAI), vol. 4953, pp. 212–221. Springer, Heidelberg (2008)
Chawla, I.: An overview of personalization in Web search. IEEE (2011); ISSN: 978-1-4244-8679-3/11/$26.00
Lee, K.-S., Park, Y.-C., Choi, K.-S.: Re-ranking model based on document clusters. Journal of Information Processing and Management 37, 1–14 (2001)
Li, Z., Xia, S., Niu, Q., Xia, Z.: Research on the User Interest Modeling of Personalized Search Engine. Wuhan University Journal of Natural Sciences 12(5) (2007)
Sufyan Beg, M.M., Ahmad, N.: Web search enhancement by mining user actions. Journal of Information Sciences 177, 5203–5218 (2007)
Arora, M., Biswas, R.: Deployment of Neutrosophic Technology to Retrieve Answer for Queries Posed in Natural Language. IEEE (2010); ISSN:978-1-4244-5540-9/10/$26.00
Choudhary, S., Choudhary, J., Dehalwar, V.: A Re-ranking Strategy for Web Search Personalization. International journal of Computer Science and its Applications (2009)
Chen, S.-Y., Zhang, Y.: Improve Web Search Ranking with Social Tagging (2009)
Lu, Y., Peng, F., Wei, X., Dumoulin, B.: Personalize Web Search Results with User’s Location. In: SIGIR 2010, July 19-23. ACM, Geneva (2010)
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Kamalanathan, S., Selvaraju, S. (2012). Collaborative Approaches for Personalized Web Search Using Fuzzy Neural Networks. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds) Global Trends in Information Systems and Software Applications. ObCom 2011. Communications in Computer and Information Science, vol 270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29216-3_40
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DOI: https://doi.org/10.1007/978-3-642-29216-3_40
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