Agent-based architecture for context-aware and personalized event recommendation

• We propose an agent-based architecture for providing contextualized recommendation.
• Personalized recommendation at the pervasive university context is provided.
• We use a semantic spreading activation algorithm to learn user patterns.
• The architecture is validated with the implementation of eAgora? application.

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.