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When number of available items surpasses the users' ability to browse them in a reasonable time, personalized recommender systems are used to assist the users to find the items that would match their interests. In this paper, the design of a context-aware recommender system for digital TV is described. Available programs are represented by their features in the adjacent vector space and genre transform is applied to reduce its dimension. Single-hidden layer feedforward neural network is used as a classifier tool which estimates whether certain program is of substantial interest to the observed user; this network is fed with the data on both program genre and the temporal context related to the user's watching habits. Special attention is paid to choosing an efficient network training algorithm and unobtrusive user feedback scheme. Good performances of the proposed system are verified through a series of experiments. It is shown that context information speeds the learning process up and reduces the so-called cold start phase without compromising the accuracy of the delivered recommendations.