Ontology-based Social Recommender System

Knowledge sharing is vital in collaborative work environments. People working in the same environment aid better communication due to sharing information and resources within a contextual knowledge structure constructed based on their scope. Social networks play important role in our daily live as it enables people to communicate, and share information. The main idea of social network is to represent a group of users joined by some kind of voluntary relation without considering any preference. This paper proposes a social recommender system that follows user’s preferences to provide recommendation based on the similarity among users participating in the social network. Ontology is used to define and estimate similarity between users and accordingly being able to connect different stakeholders working in the community field such as social associations and volunteers. This approach is based on integration of major characteristics of content-based and collaborative filtering techniques. Ontology plays a central role in this system since it is used to store and maintain the dynamic profiles of the users which is essential for interaction and connection of appropriate knowledge flow and transaction.