An Ontology-based Approach on Intelligent Recommendation in Movie Field

In recent years, we have witnessed an exponential growth in the amount of information available in diverse domains (like E-Commerce). This overload is hard to digest for users, who have difficulties to find really relevant items. Intelligent Recommendation systems emerged in this background, and have brought a huge improvement in recommending the user interesting items. However, the current recommendation techniques, such as Content Filtering and Collaborative Filtering, have not considered the similarities between user profiles and texts using the user activity data, and lack of user evaluation information in the beginning of system. This paper proposes a novel ontology-based intelligent recommendation approach, which establishes the movie and user ontologies, calculates semantic similarities using a series of algorithms, and realizes the intelligent recommendation in movie field. A prototype system named 'OIRES' has been implemented in light of this approach.