Abstract
Search engine optimization is an interesting research issue in information retrieval for retrieving user interesting results. Satisfying the user search goal is a complex task while searching user specific query, because of billions of related and unrelated data available over the network. In this proposed work we are introducing an empirical model of search mechanism with FP Tree for finding frequent use of patterns (sequence of Urls) and genetic algorithm, which belongs to the larger class of evolutionary algorithms, which will be used for generating solutions for optimization problems using techniques such as mutation and crossover for optimal results with efficient feedback sessions, based on query clicks. From the time user starts clicking or visiting the urls the session is started and the session of feedback is generated reflecting the efficient user information needs.
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References
Pu, H.-T., Chiao, H.-C.: Web relevant term suggestion Using Log-based and Text-based Approaches (December 2006)
Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering General Time-Sensitive Queries. IEEE Transactions on Knowledge and Data Engineering Member 24(2) (2012)
Hwang, H., Lauw, H.W., Getoor, L., Ntoulas, A.: Organizing User Search Histories. IEEE Transactions on Knowledge and Data Engineering (May 2012)
Wang, X., Zhai, C.-X.: Learn from Web Search Logs to Organize Search Results. In: Proc. 30th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2007), pp. 87–94 (2007)
Lu, Z., Zha, H., Yang, X., Member, S., Lin, W., Zheng, Z.: A New Algorithm for Inferring User Search Goals with Feedback Sessions. IEEE Transaction on Knowledge and Data Engineering 25(3) (2013)
Joachims, T.: Optimizing Search Engines Using Click through Data. In: Proc. Eighth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD 2002), pp. 133–142 (2002)
Wen, J.-R., Nie, J.-Y., Zhang, H.-J.: Clustering User Queries of Search Engine. In: Proc. Tenth Int’l Conf. World Wide Web (WWW 2001), pp. 162–168 (2001)
Beeferman, D., Berger, A.: Agglomerative Clustering of a Search Engine Query Log. In: Proc. Sixth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD 2000), pp. 407–416 (2000)
Beitzel, S., Jensen, E., Chowdhury, A., Frieder, O.: Varying Approaches to Topical Web Query Classification. In: Proc. 30th International ACM SIGIR Conference of Research and Development (SIGIR 2007), pp. 783–784 (2007)
Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., Li, H.: Context-Aware Query Suggestion by Mining Click-Through. In: Proc. 14th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD 2008), pp. 875–883 (2008)
Lee, U., Liu, Z., Cho, J.: Automatic Identification of User Goals in Web Search. In: Proc. 14th Int’l Conf. World Wide Web (WWW 2005), pp. 391–400 (2005)
Baeza-Yates, R., Hurtado, C.A., Mendoza, M.: Query Recommendation Using Query Logs in Search Engines. In: Lindner, W., Fischer, F., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 588–596. Springer, Heidelberg (2004)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation, http://Web.engr.illinois.edu/~hanj/pdf/sigmod00.pdf
Mitchell, T.: An introduction to Genetic Algorithms Melanie
Li, X., Wang, Y.-Y., Acero, A.: Learning Query Intent from Regularized Click Graphs. In: Proc. 31st Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2008), pp. 339–346 (2008)
Shen, J., Sun, Q.: Building Bridges for Web Query Classification. In: Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR 2006), pp. 131–138 (2006)
Chen, H., Dumais, S.: Bringing Order to the Web: AutomaticallyCategorizing Search Results. In: Proc. SIGCHI Conf. HumanFactors in Computing Systems (SIGCHI 2000), pp. 145–152 (2000)
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Sharief, M.N., Yadwad, S.A. (2015). An Integrating Approach by Using FP Tree and Genetic Algorithm for Inferring User Interesting Results. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_6
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DOI: https://doi.org/10.1007/978-3-319-11933-5_6
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