SAHN with SEP/COP and SPADE, to Build a General Web Navigation Adaptation System Using Server Log Information
During the last decades, the information on the web has increased drastically but larger quantities of data do not provide added value for web visitors; there is a need of easier access to the required information and adaptation to their preferences or needs. The use of machine learning techniques to build user models allows to take into account their real preferences. We present in this work the design of a complete system, based on the collaborative filtering approach, to identify interesting links for the users while they are navigating and to make the access to those links easier. Starting from web navigation logs and adding a generalization procedure to the preprocessing step, we use agglomerative hierarchical clustering (SAHN) combined with SEP/COP, a novel methodology to obtain the best partition from a hierarchy, to group users with similar navigation behavior or interests. We then use SPADE as sequential pattern discovery technique to obtain the most probable transactions for the users belonging to each group and then be able to adapt the navigation of future users according to those profiles. The experiments show that the designed system performs efficiently in a web-accesible database and is even able to tackle the cold start or 0-day problem.
KeywordsUser Session Good Partition Cluster Validity Index Navigation Pattern Navigation Sequence
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