Abstract
Web recommendation systems based on web usage mining try to mine users’ behavior patterns from web access logs, and recommend pages to the online user by matching the user’s browsing behavior with the mined historical behavior patterns. Recommendation approaches proposed in previous works, however, do not distinguish the importance of different pageviews, and all the visited pages are treated equally whatever their usefulness to the user. We propose to use pageview duration to judge its usefulness to a user, and try to give more consideration to more useful pageviews, in order to better capture the user’s information need and recommend pages more useful to the user. In this paper we try to incorporate pageview weight into the Association Rule (AR) based model and develop a Weighted Association Rule (WAR) model. Comparative experiment of the two shows a significant improvement in the recommendation effectiveness with the proposed WAR model.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yan, L., Li, C. (2006). Incorporating Pageview Weight into an Association-Rule-Based Web Recommendation System. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_62
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DOI: https://doi.org/10.1007/11941439_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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