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As huge number of traditional TV programs and on demand video streams offered via Internet is now simultaneously available through hybrid broadcast broadband television, the search for an interesting content often turns into a time-consuming task for a viewer. In a situation like this, both the providers and the viewers would benefit from personalized recommender systems. The choice of neural network architecture and learning algorithm is mainly influenced by users’ privacy concerns and characteristics of data collected from user interactions. In this session, it will be discussed how to overcome these challenges by using feedforward neural network trained by cost-sensitive version of Extreme Learning Machine (ELM) algorithm and sparse ELM autoencoder trained with fast iterative shrinkage-thresholding algorithm, considering cases with and without “dislike” interactions, respectively. Through a series of tests it will be shown that proposed solutions improve system performance and consequently increase users’ satisfaction."