Enhancing news recommendation using a personalized content manager

Abstract

The surge in the amount of information available on the internet and the number of users utilizing such information poses new challenges for information systems. This rapid growth in information is palpable in the news provisioning domain where users spend time deciding which of the many channels provides news in a most reliable and useful fashion. In the last few years, News aggregation platforms are now present which reduces the time users spends in consuming news from multiple sources. But news provisioning is more than just aggregating and presenting news. It must also include taking into cognizance users’ needs. Therefore, recommendation algorithms are developed by information systems and news contents are recommended and personalized for users while utilizing user’s specific data. If content recommendation is to be optimal, better and more efficient algorithms must be developed and implemented. Challenges associated with this type information systems include providing quality and novel information, feeding out relevant information while dealing with problems such as ‘data-sparsity’ commonly associated with recommending content. To this end I conduct a study which employs a hybrid approach to solving the existing problems with recommendation system and providing quality, relevant and serendipitous news content for users.