Personalized news recommendation in indonesian language based on user click behavior
The enormous amount of information available today has forced users to face an information overload, such as overwhelming volumes of articles. Users have to endure tremendous volumes of information to find their desired articles. Personalized recommendation system has been proposed for years as the solution for this information era problem. This research strives to develop an intelligent personalized recommendation system based on user click behavior on news websites. We extracted frequent itemsets and association rules from the web server log of a news website, performed a pre-computation of similarity between news articles, and then proposed a three-level recommendation system: based on association rule discovery, news articles on the same category, and similarity between news articles. By combining collaborative filtering approach and content-based filtering, experiment results show that the technique produces reliable news recommendation.
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