Abstract

We describe the Universal Recommender, a recommender system for semantic datasets that generalizes domain-specific recommenders such a 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.

@misc{kunegis2009c,
	title = {The {Universal Recommender}},
	author = {Jérôme Kunegis and Alan Said and Winfried Umbrath},
	year = {2009}, 
	howpublished = {White paper}, 
}