Content based recommendation for HBB TV based on bayes conditional probability for multiple variables approach

The amount of available content of different types of services is so large nowadays that one cannot realistically have a real time overview of the content. 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 an enhanced Naïve Bayes model for rating prediction of a program based on content description information. As our prediction model has to deal with categorical data, a probabilistic Bayesian network is used. The model uses a set of features to predict user rating based on past observation. We also simulated recommendation from a program offer. 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 were performed on a Yahoo movie data set and they indicated the promising performance of our approach over an existing technique.