Personalized Recommendation Based on Ontology Inference in e-Commerce

With the rapid development of Internet, personalized information service has become one of the hotspots in e-commerce. In this paper, we explore a novel approach to use ontology inference in personalized recommendation, working on the problem of recommending on-line commodity. We organize on-line commodity in terms of ontological classes and using ontological inference as our recommendation algorithm. Ontology inference is shown to improve user profiling, forecast user preference, and enhance recommendation accuracy. The overall performance of our ontological personalized recommendation algorithm presents better compared to other systems.