Impact of class imbalance on personalized program guide performance

When TV recommender systems perform well, number of interactions in which their users expressed positive feedback on the recommended content is expected to be greater than the number of negative ones. This is known as class imbalance and, paradoxically, it degrades the system performance by making the identification of the programs the user will dislike increasingly difficult. As the misclassification of the unwanted content is easily perceived by TV viewers, it should be avoided by all means. In this paper, a personalized TV program guide based on neural network is described. It is shown how class imbalance information can be exploited in learning the user preferences. This not only improves the system performance, but increases the user satisfaction as well¹.