  We describe the Universal Recommender, a recommender system for semantic
datasets that generalizes domain-specific recommenders such as content-based,
collaborative, social, bibliographic, lexicographic, hybrid and other
recommenders. In contrast to existing recommender systems, the Universal
Recommender applies to any dataset that allows a semantic representation. We
describe the scalable three-stage architecture of the Universal Recommender and
its application to Internet Protocol Television (IPTV). To achieve good
recommendation accuracy, several novel machine learning and optimization
problems are identified. We finally give a brief argument supporting the need
for machine learning recommenders.
