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Paradoxically, a growing number of available channels in digital cable TV systems brings discomfort to the viewers who now experience difficulties in finding a content that would hold their attention. In such an environment, personalized program guides are needed to assist the viewers in retrieving the preferred programs in reasonable time. The design of these systems is bounded by the demand of unobtrusiveness and the limitations of broadcast infrastructure, with the lack of return (uplink) connection to the network center being the most significant one. In this paper, we investigate learning of user's viewing preferences through mechanism known as relevance feedback. Our goal is to develop a system that would efficiently track the patterns of user's interests without disturbing her viewing habits. Our proposal applies the elements of machine learning and information retrieval theory. We consider three different schemes and validate their performances by series of computer simulations.