Personalized advertisement system using social relationship based user modeling

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight ‘1’ is given to those who have connections directly, and weight ‘0’ is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User’s types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.