01654nas a2200133 4500008004100000245006400041210006400105300001200169490000600181520117600187100002101363700002201384856011401406 2017 eng d00aImproving news articles recommendations via user clustering0 aImproving news articles recommendations via user clustering a223-2370 v83 a

Although commonly only item clustering is suggested by Web mining techniques for news articles recommendation systems, one of the various tasks of personalized recommendation is categorization of Web users. With the rapid explosion of online news articles, predicting
user-browsing behavior using collaborative filtering (CF) techniques has gained much attention in the web personalization area. However common CF techniques suffer from problems like low accuracy and performance. This research proposes a new personalized recommendation approach that integrates both user and text clustering based on our developed algorithm, W-kmeans, with other information retrieval (IR) 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 item and
user clustering with multiple IR techniques like categorization and summarization, our recommender generates results that outperform the cases where each or both of them are used, but clustering is not applied.

1 aBouras, Christos1 aTsogkas, Vassilis uhttps://telematics.upatras.gr/telematics/publications/improving-news-articles-recommendations-user-clustering