Recommendation systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommendation systems, content-based recommendation systems and a few hybrid systems. We propose a semantic framework to overcome common limitations of current systems. We present a system whose representations of items and user-profiles are based on concept taxonomies in order to provide personalized recommendation and services. The recommender incorporates semantics to enhance (1) user modeling by applying a domain-based inference method, and (2) recommendation by applying a semantic-similarity method. We show that semantics can often be used to overcome information scarcity. Experiments on movie-data from Netflix show that systems incorporating semantics produce significantly better quality recommendations than content-based ones.