Algorithms for clustering data
Algorithms for clustering data
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
ACM SIGKDD Explorations Newsletter
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
User Modeling and User-Adapted Interaction
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
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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.