User interest expansion using spreading activation for generating recommendations

In this paper, a novel user interest expansion approach for generating recommendations is proposed. The approach utilizes interest of user as well as semantic relatedness between the items along with context to generate recommendations. Ontologies are used to represent domain knowledge. Spreading activation technique uses the relatedness between concepts of user interest in domain ontology to expand user interest which results in generation of diverse recommendations. A prototype of the system has been designed and developed using various JAVA technologies for Restaurant domain and its performance is evaluated using precision, recall, F1 and diversity metrics. Performance of the proposed Context Aware Recommender System with Expansion of user interest (ECARS) is compared with Context Aware Recommender System (CARS) and Content Based Recommender System (CB) for generating recommendations.