Hybrid broadcast and broadband (HBB) television is a new platform that opens many possibilities for new services. Recommendation system offers a personalized service that suggests items of interest according to user preference. Nowadays, the number of available programs is so large that one cannot realistically have a real time overview. Recommendation engines were developed to solve the problem of information overload, and save time and effort when looking for appealing content. In this paper, we present model design and implementation of a recommendation system for HBB TV. To explore user preferences and make predictions, an enhanced Naïve Bayes model for rating prediction is designed. The model uses a set of features to predict user rating based on past observation. The recommendation system presented in this paper is flexible and robust enough to handle a sparse data set with very few records of feature description. Experiments performed on a Yahoo movie data set indicated the promising performance of our approach.