Agent-Based Customer Profile Learning in 3G Recommender Systems: Ontology-Driven Multi-source Cross-Domain Case

Advanced recommender systems of the third generation (3G) emphasize employment of semantically clear models of customer cross-domain profile learned using all available data sources. The paper focuses on conceptual level of ontology-based formal model of the customer profile built in actionable form. Learning of cross-domain customer profile as well as its use in recommendation scenario requires solving a number of novel problems, e.g. information fusion and data source privacy preservation, among others. The paper proposes an ontology-driven personalized customer profile model and outlines an agent-based architecture supporting implementation of interaction-intensive agent collaboration in two variants of target decision making procedure that are content-based and collaborative filtering both exploiting semantic similarity measures.