Adaptive feedback schemes for personalized content retrieval

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.