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The information age is characterized by an
overabundance of information but few tools to help
users of information spaces get just the right
information as they navigate these spaces. Current
research thus aims to design tools like recommender
systems to personalize the user experience in large
information spaces. This book explores factors
involved in hyperlink recommender systems design with
a view to improving on them or proposing new
solutions. The work makes the following
contributions in the area of data mining.
Web Page Classification: a new classification metric
is developed; Association rule mining: a new apriori
algorithm is developed; Prediction model for user
interests: methodology for extracting from web
server logs, association rules that show
correlations between user navigation patterns and
interesting web pages, and transformation of the
rules into collaborative filtering data, widely used
in recommender systems; Clustering: a comparative
study of the CLARANS, PAM, and CLARA algorithms in
high dimensional space is presented. The work should
therefore be of interest to anyone involved in
personalization systems and data mining research.
overabundance of information but few tools to help
users of information spaces get just the right
information as they navigate these spaces. Current
research thus aims to design tools like recommender
systems to personalize the user experience in large
information spaces. This book explores factors
involved in hyperlink recommender systems design with
a view to improving on them or proposing new
solutions. The work makes the following
contributions in the area of data mining.
Web Page Classification: a new classification metric
is developed; Association rule mining: a new apriori
algorithm is developed; Prediction model for user
interests: methodology for extracting from web
server logs, association rules that show
correlations between user navigation patterns and
interesting web pages, and transformation of the
rules into collaborative filtering data, widely used
in recommender systems; Clustering: a comparative
study of the CLARANS, PAM, and CLARA algorithms in
high dimensional space is presented. The work should
therefore be of interest to anyone involved in
personalization systems and data mining research.