Personalized Mobile Catering Recommender System Based on Context Ontology Model and Rule Inference

Abstract

With the development of smart mobile terminals and pervasive computing, mobile recommender systems are proposed to realize context-aware personalized recommendation services. A context model plays a key role in a mobile recommender. We discussed the context ontology modeling approach specific for mobile recommendation, and developed a two-level context model including a upper ontology and a domain ontology. We also designed a personalized mobile catering recommender system based on the context ontology model and rule inference. The framework of the system is depicted. And the process of rule generation and rule inference based on the context ontologies is demonstrated.