Providing Context-Aware Personalization through Cross-Context Reasoning of User Modeling Data

Existing personalization systems base their services on user models that typically disregard the issue of context-awareness. This work focuses on developing mechanisms for cross-context reasoning, i.e., inferences linking user model data in two different contexts. That reasoning process can augment the typically sparse user models, by inferring the missing information from other contextual conditions, and can better support context-aware personalization. Thus, the proposed approach improves existing personalization systems and facilitates provision of more accurate context-aware personalized services.