Abstract
In this work a twofold algorithmic framework for the adaptation of web content to the users’ choices is presented. The main merits discussed are a) an optimal offline site adaptation – reorganization approach, which is based on a set of different popularity metrics and, additionally, b) an online personalization mechanism to emerge the most “hot” (popular and recent) site subgraphs in a recommendation list adaptive to the users” individual preferences.
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Makris, C., Panagis, Y., Sakkopoulos, E., Tsakalidis, A. (2006). An Algorithmic Framework for Adaptive Web Content. In: Sirmakessis, S. (eds) Adaptive and Personalized Semantic Web. Studies in Computational Intelligence, vol 14. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-33279-0_1
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DOI: https://doi.org/10.1007/3-540-33279-0_1
Publisher Name: Springer, Berlin, Heidelberg
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