Towards TV recommender system: experiments with user modeling

As the number of cable TV programs grows, it becomes more difficult for the viewers to find the right one. This calls for specialized recommender systems, often in a form of electronic program guides, which should provide unobtrusive assistance. In this paper, we analyze such recommender system design under the broadcast scenario, where uplink connection to the network center is not available. We put special emphasis on user modeling algorithm that would be able to efficiently learn the user's interests. Our proposal applies the elements of machine learning and pattern recognition, as well as the information retrieval theory, like vector spaces and cluster hypothesis. The derived algorithm is computationally simple, while experimental results show high acceptance ratio of the proposed recommendations.