Abstract
Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users’ transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contained in the active user profile will be calculated upon the matched rules and finally the top-N most similar items are then recommended to the user. We used the time spent on each page for weighting the pages instead of binary. Two evaluation metrics were applied to evaluate the accuracy of the recommendations, namely precision and coverage.
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© 2004 Springer-Verlag Berlin Heidelberg
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Ahmad, A.M., Hijazi, M.H.A. (2004). Web Page Recommendation Model for Web Personalization. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_77
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DOI: https://doi.org/10.1007/978-3-540-30133-2_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23206-3
Online ISBN: 978-3-540-30133-2
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