SERUM: Collecting Semantic User Behavior for Improved News Recommendations
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How can semantic data and semantic technologies be leveraged for personalization and recommendation services? In this paper, we present SERUM (Semantic Recommendations based on large unstructured datasets), a news recommendation system that utilizes semantic technologies to collect implicit user behavior and to build semantic user models. These models, combined with large-scale semantic datasets, are then used to compute personalized news recommendations using graph-based algorithms. We introduce the building blocks of SERUM for the semantic data management, personalization and recommendation, with the main focus on the implicit user behavior collection. Therefore, our system uses RDFa to collect meaningful user behavior and a self-developed user behavior ontology (the User Behavior Ontology, in short UBO) to build semantic user behavior models. The main contribution of this work is the introduction of the UBO and the associated semantic user tracking and modeling process.
Keywordsuser behavior semantic recommendation graph based recommender news personalization
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