News Selection with Topic Modeling

There are numerous news articles coming to news aggregators and important news are selected to be presented on the front-page. There are two types of news selection for the front-page of news aggregators: personalized and public news recommendation (selection). This study examines public news recommendation that aims to satisfy all users’ interest on the front-page. Public news recommendation is mainly done by meta-features like news popularity. A different approach that exploits the news content is introduced in this work. The main target is to select important (significant) news articles while providing diversification in the selected news topics. A new approach based on topic modeling is developed for this purpose. Results show that it is hard to achieve satisfactory level of precision when content-based public news recommendation is applied. However, precision of topic modeling-based approach is noticeably better than precision of random news recommendation. Topics of selected news are also diversified by using topic modeling.