@conference {3006,
	title = {User Personalization via W - kmeans},
	booktitle = {KES2012 - The 16th International Conference on Knowledge Based \& Intelligent Information \& Engineering Systems, San Sebastian, Spain},
	year = {2012},
	month = {10-12 September},
	pages = {555 - 564},
	abstract = {With the rapid explosion of online news articles, predicting userbrowsing
behavior using collaborative filtering techniques has gained much attention
in the web personalization area. However, common collaborative filtering
techniques suffer from low accuracy and performance. This research proposes
a new personalized recommendation approach that integrates user and
text clustering based on our developed algorithm, W-kmeans, with other information
retrieval techniques, like text categorization and summarization in order
to provide users with the articles that match their profiles. Our system can easily
adapt over time to divertive user preferences. Furthermore, experimental results
show that by aggregating multiple other information retrieval techniques
like categorization, summarization and clustering, our recommender generates
results that outperform the cases when clustering is not applied.},
	author = {Christos Bouras and Vassilis Tsogkas}
}
