We consider personalized content retrieval in a resource-constrained multiservice environment with broadcast TV acting as a model usage scenario. We propose personalized recommender system that captures the user's viewing habits without obstructing the usual way TV is watched. Our proposal describes program representation and retrieval, user modeling, and aggregation of her/his estimated interests by adaptive feedback schemes. Through series of experiments with TV viewing application, we show that our proposal promptly learns the user's preferences and delivers valued recommendations.