Abstract
This paper presents a semantic recommender method and a system for a personalized access to digital cultural heritage through context-aware user profiling. Given annotation knowledge-bases, explicit background knowledge in the form of ontologies, a user model capturing the user’s behavior and context, the system produces recommendations. Ontology-based user profiling can be used to reduce cold-start, sparsity and over-specialization problems. In addition, we present a recommendation retrieval method that is based on the vector space model and uses indices that enable fast and scalable implementation of the system.
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