How to cite this paper
APA: Rashidi, S., Harounabadi, A & Dezfouli, M. (2012). Prediction of users’ future requests using neural network. Management Science Letters, 2(6), 2119-2124.
Chicago/Turabian: Rashidi, S., Harounabadi, A & Dezfouli, M. 2012. "Prediction of users’ future requests using neural network." Management Science Letters 2, no. 6 (2012): 2119-2124.
AMA: Rashidi, S., Harounabadi, A & Dezfouli, M. Prediction of users’ future requests using neural network. Management Science Letters. 2012;2(6):2119-2124.
References
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Goksedef, M. & Gunduz-Oguducu, S. (2007). A Consensus Recommender for Web Users, In: ADMA. The 3rd international conference on advance data mining and applications, 287–299.
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Li, J. & Zaiane, O. R. (2004). Combining usage, content, and structure data to improve web site recommendation. In: Proceedings of 5th international conference on electronic commerce and web, 305–315).
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Liu, H. & Keselj, V. (2007). Combined mining of web server logs and web contents For classifying user navigation patterns and predicting user’s future requests. Data and Knowledge Engineering, 61(2), 304-330.
Madria S. K., et al., (1999), Research Issues in Web Data Mining. In: Proceedings of Data Warehousing and Knowledge Discovery, First International Conference, DaWaK & apos; 99. 1999, p. 303-312, Florence, Italy.
Mobasher, B., Dai, H., Luo, T. & Nakagawa, M. (2001). Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd International Workshop on Web Information and Data Management, ACM Press, Atlanta, GA, USA, 9–15.
Mobasher B. (2004). Web Usage Mining and Personalization, Practical Handbook of Internet Computing. Chapman Hall and CRC Press.
Nakagawa, M. & Mobasher, B. (2003). A hybrid web personalization model based on site connectivity. Proceedings of WebKDD, 59–70.
Phatak, D. S., & Mulvaney, R. (2002). Clustering For personalized mobile web usage. Proceedings of the IEEE FUZZ’02, Hawaii, USA, 705–710.
Srivastava, J. Cooley, R., Deshpande, M., & Tan, P.-N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 12-23.
Zhou, B., Hui, S.C. & Chang, K. (2004). An intelligent recommender system using sequential Web access patterns. Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1–3.
Bouckaert, R.R., et al. (2008), WEKA Manual for Version 3-6-0.
He, J., Tan, A. H., Tan, C. L., & Sung, S. Y., (2003). On quantitative evaluation of clustering systems. Information Retrieval and Clustering, 105–134.
Baglioni M., Ferrara U., Romei A., Ruggieri S. & Turini F. (2003). Preprocessing and mining Web log data for web personalization, In: AI*IA, 237–249.
Basheer, I. A., & Hajmeer, M. (2000), Artificial neural networks: Fundamentals, computing, design and application. Journal of Microbiological Methods, 43, 3–31.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.
Cooley, R., Mobasher, R. & Srivastava, J. (1999). Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems, 1(1), 5–32.
Dumais, S., Joachims, T., Bharat, K. & Weigend, A. (2003). Workshop Report: Implicit Measures of User Interests and Preferences, ACM SIGIR Forum.
Dunham, M. H. (2003). Data Mining: Introductory and Advanced Topics. Prentice Hall.
Goksedef, M. & Gunduz-Oguducu, S. (2007). A Consensus Recommender for Web Users, In: ADMA. The 3rd international conference on advance data mining and applications, 287–299.
Xu, G. (2008). Web Mining Techniques for Recommendation and Personalization, PhD thesis, Victoria University.
Jain, A.K., Murty, M.N., & Flynn, P.J. (2000). Data clustering: A review. ACM Computer, 31(3) 264-323.
Li, J. & Zaiane, O. R. (2004). Combining usage, content, and structure data to improve web site recommendation. In: Proceedings of 5th international conference on electronic commerce and web, 305–315).
Lin, W., Alvarez, S. A., & Ruiz, C. (2000). Collaborative recommendation via adaptive association rule mining. Web Mining for E-Commerce – Challenges and Opportunities, Second International Workshop, Boston, MA, USA.
Liu, H. & Keselj, V. (2007). Combined mining of web server logs and web contents For classifying user navigation patterns and predicting user’s future requests. Data and Knowledge Engineering, 61(2), 304-330.
Madria S. K., et al., (1999), Research Issues in Web Data Mining. In: Proceedings of Data Warehousing and Knowledge Discovery, First International Conference, DaWaK & apos; 99. 1999, p. 303-312, Florence, Italy.
Mobasher, B., Dai, H., Luo, T. & Nakagawa, M. (2001). Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd International Workshop on Web Information and Data Management, ACM Press, Atlanta, GA, USA, 9–15.
Mobasher B. (2004). Web Usage Mining and Personalization, Practical Handbook of Internet Computing. Chapman Hall and CRC Press.
Nakagawa, M. & Mobasher, B. (2003). A hybrid web personalization model based on site connectivity. Proceedings of WebKDD, 59–70.
Phatak, D. S., & Mulvaney, R. (2002). Clustering For personalized mobile web usage. Proceedings of the IEEE FUZZ’02, Hawaii, USA, 705–710.
Srivastava, J. Cooley, R., Deshpande, M., & Tan, P.-N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2), 12-23.
Zhou, B., Hui, S.C. & Chang, K. (2004). An intelligent recommender system using sequential Web access patterns. Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, 1–3.
Bouckaert, R.R., et al. (2008), WEKA Manual for Version 3-6-0.
He, J., Tan, A. H., Tan, C. L., & Sung, S. Y., (2003). On quantitative evaluation of clustering systems. Information Retrieval and Clustering, 105–134.