How to cite this paper
khademali, Z., Harounabadi, A & mirabedini, J. (2013). A new intelligent algorithm to create a profile for user based on web interactions.Management Science Letters , 3(4), 1155-1160.
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log analysis. Journal of documentation, 61(2), 248-280.
Nicholas, D., Huntington, P., Jamali, H. R., & Tenopir, C. (2006). Finding information in (very large)
digital libraries: a deep log approach to determining differences in use according to method of
access. The Journal of academic librarianship, 32(2), 119-126.
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behaviour of the users of digital scholarly journals.Information Processing & Management, 42(5),
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Phatak, D. S., & Mulvaney, R. (2002). Clustering for personalized mobile web usage. In Fuzzy
Systems, 2002. FUZZ-IEEE & apos; 02. Proceedings of the 2002 IEEE International Conference on (Vol.
1, pp. 705-710). IEEE.
Spiliopoulou, M., Mobasher, B., Berendt, B., & Nakagawa, M. (2003). A framework for the
evaluation of session reconstruction heuristics in web-usage analysis. INFORMS Journal on
Computing, 15(2), 171-190.
survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE
Transactions on, 17(6), 734-749.
Breeding, M. (2005). Analyzing web server logs to improve a site & apos; s usage. The Systems
Librarian. Computers in Libraries, 25(9), 26-28.
Berendt, B., Mobasher, B., Spiliopoulou, M., & Wiltshire, J. (2001). Measuring the accuracy of
sessionizers for web usage analysis. In Workshop on Web Mining at the First SIAM International
Conference on Data Mining (pp. 7-14).
Cooley, R., Mobasher, B., & Srivastava, J. (1999). Data preparation for mining world wide web
browsing patterns. Knowledge and information systems, 1(1), 5-32.
Forsati, R., Meybodi, M.R. (2009). Algorithmic based on structure pages and user’s usage
information for recommending web pages. The 2th Iran data mining conference.
Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. ACM Sigkdd Explorations
Newsletter, 2(1), 1-15.
Lin, W., Alvarez, S. A., & Ruiz, C. (2000). Collaborative recommendation via adaptive association
rule mining. In Proceedings of the International Workshop on Web Mining for E-Commerce
(WEBKDD.
Mobasher, B. (1999). A Web personalization engine based on user transaction clustering.
In Proceedings of the 9th Workshop on Information Technologies and Systems (Vol. 18).
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 (pp. 9-15). ACM.
Mohamadi Dostdar, H., Forsati, R., & Meybodi, M.R. (2011) Recommender system of combined web
based on 2-layers graph and partition of graph, The 5th Iran data mining conference.
Nicholas, D., Huntington, P., & Watkinson, A. (2005). Scholarly journal usage: the results of deep
log analysis. Journal of documentation, 61(2), 248-280.
Nicholas, D., Huntington, P., Jamali, H. R., & Tenopir, C. (2006). Finding information in (very large)
digital libraries: a deep log approach to determining differences in use according to method of
access. The Journal of academic librarianship, 32(2), 119-126.
Nicholas, D., Huntington, P., Jamali, H. R., & Watkinson, A. (2006). The information seeking
behaviour of the users of digital scholarly journals.Information Processing & Management, 42(5),
1345-1365.
Phatak, D. S., & Mulvaney, R. (2002). Clustering for personalized mobile web usage. In Fuzzy
Systems, 2002. FUZZ-IEEE & apos; 02. Proceedings of the 2002 IEEE International Conference on (Vol.
1, pp. 705-710). IEEE.
Spiliopoulou, M., Mobasher, B., Berendt, B., & Nakagawa, M. (2003). A framework for the
evaluation of session reconstruction heuristics in web-usage analysis. INFORMS Journal on
Computing, 15(2), 171-190.