Personalized program guide based on one-class classifier

As TV viewers inherently tend to avoid contents they might dislike, they do not provide equal amounts of positive and negative feedback on their viewing preferences. At the same time, mobile devices are becoming target platforms for multimedia content delivery. Personalized program guides need to cope with these challenges. They must not only properly recognize the undesired content, despite the lack of the learning data, but also provide valuable recommendations without compromising the user's privacy. Moreover, the complexity of the applied algorithms has to be low enough to match the limited hardware resources of the mobile terminals. In this paper, the design of such program guide is described. Several system architectures are developed and compared. The best performance is achieved for a single hidden layer autoencoder neural network trained with the FISTA algorithm
¹
.