@conference {2994,
	title = {Clustering user preferences using W - kmeans},
	booktitle = {The 7th International Conference on Signal Image Technology \& Internet   Based Systems (SITIS 11), Dijion - France},
	year = {2011},
	month = {November 28 - December 1},
	pages = { 75 - 82},
	abstract = {Abstract{\textemdash} Although commonly only document clustering is
suggested by Web mining techniques for recommendation
systems, one of the various tasks of personalized
recommendation is categorization of Web users. In this paper,
a method for clustering navigation patterns of Web users is
proposed. We adapt the WordNet-enabled W-kmeans
algorithm, an enhancement of standard k-means algorithm
which uses the external knowledge from WordNet hypernyms
and that has been previously used for document clustering, to
user profile clustering by analyzing the users? historical data.
We also investigate the effects this approach has on the
recommendation engine by evaluating the overall performance
it has in terms of precision {\textendash} recall on our online
recommendation system.},
	author = {Christos Bouras and Vassilis Tsogkas}
}
