Abstract
Personalization systems based upon the analysis of users’ surfing behavior imply three phases: data collection, pattern discovery and recommendation. Due to the dimension of log files and high processing time, the first two phases are achieved offline, in a batch process. In this article, we propose Wise Recommender System (WRS), an architecture for adaptive web applications. Within this framework, usage data is implicitly obtained by the data collection submodule. This allows for the extraction of usage data, online and in real time, by using a proactive approach. For the pattern discovery, we efficiently used association rule mining among both frequent and infrequent items. This is due to the fact that the pattern discovery module transactionally processes users’ sessions and uses incremental storage of rules. Finally, we will show that WRS can be easily implemented within any web application, thanks to the efficient integration of the three phases into an online transactional process.
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Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C, pp. 207–216 (1993)
Baraglia, R., Silvestri, F.: Dynamic personalization of web sites without user intervention. ACM Commun. 50(2), 63–67 (2007)
Bayir, M.A., Toroslu, I.H., Cosar, A., Fidan, G.: Smart Miner: a new framework for mining large scale web usage data. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 161–170. ACM, New York (2009)
Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the World-Wide Web. Comput. Netw. ISDN Syst. 27(6), 1065–1073 (1995)
Ceglar, A., Roddick, J.F.: Association mining. ACM Comput. Surv. 38(2) (2006)
Chen, M.S., Park, J.S., Yu, P.S.: Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering, 209–221 (1998)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining World Wide Web browsing patterns. Knowledge Information Systems 1(1), 5–32 (1999)
Ding, J., Yau, S.S.: TCOM, an innovative data structure for mining association rules among infrequent items. Comput. Math. Appl. 57(2), 290–301 (2009)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on Web usage mining. ACM Commun. 43(8), 142–151 (2000)
Perkowitz, M., Etzioni, O.: Adaptive sites: Automatically learning from user access patterns. In: Proc. of the Sixth International WWW Conference, Santa Clara, CA (1997)
Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis. INFORMS J. on Computing 15(2), 171–190 (2003)
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Mican, D., Tomai, N. (2010). Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications. In: Daniel, F., Facca, F.M. (eds) Current Trends in Web Engineering. ICWE 2010. Lecture Notes in Computer Science, vol 6385. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16985-4_8
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DOI: https://doi.org/10.1007/978-3-642-16985-4_8
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