News platforms exhibit both the challenges as well as opportunities for enhancing the functionalities of recommendation systems in today's big data environment. Novel use of big data storage and programming models can improve news recommendation systems through efficient handling and analysis of clickstream data and a better understanding of users' interests. Most existing approaches to news recommendation consider users' clicks as the implicit feedback to understand user behaviors. However, “clicks” may not be an effective indicator of real user interests. We address this problem by developing a novel news recommendation system based on a news utility model. Given the new utility model, we propose a two stage news recommendation framework. The framework first generates article-level recommendation rules based on the utility model, then integrates the notion of utility and probabilistic topic models and generates topic-level recommendation rules. We argue that the proposed utility-based news recommendation system also addresses the news cold start problem which is one of the most challenging obstacles for news agencies. We evaluate the framework on a massive real dataset (two billion records) obtained from a major newspaper (i.e., The Globe and Mail) in Canada and show that it outperforms the existing methods.