Using User Personalized Ontological Profile to Infer Semantic Knowledge for Personalized Recommendation

A key feature in developing an effective web personalization system is to build and model a dynamic user profiles. In this paper, we propose a novel method to construct user personalized ontological profiles based on each user's interests and view. We also propose an Enhanced Spreading Activation Technique (ESAT) to infer and recommend new items to a user based on each user's personalized ontological profile. Using the MovieLens dataset, we show that our approach achieves the highest prediction accuracy, and outperforms other recommendation approaches that were proposed in the literature. © 2011 Springer-Verlag.