User Personalization via W-kmeans

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.