Improving the effectiveness of collaborative recommendation with ontology-based user profiles

Collaborative recommendation is effective at representing a user’s overall interests and tastes, and finding peer users that can provide good recommendations. However, it re-mains a challenge to make collaborative recommendation sensitive to a user’s specific context and to the changing shape of user interests over time. Our approach to building context-sensitive collaborative recommendation is a hybrid one that incorporates semantic knowledge in the form of a domain ontology. User profiles are defined relative to the ontology, giving rise to an ontological user profile. In this paper, we describe how ontological user profiles are learned, incrementally updated, and used for collaborative recom-mendation. Using book rating data, we demonstrate that this recommendation algorithm offers improved coverage, di-versity, personalization, and cold-start performance while at the same time enhancing recommendation accuracy