Improved Internet Resource Recommendation Method using FOAF and SNA

In recent years, due to rapidly increasing user-created internet contents coupled with the development of community-based websites, the internet resource recommendation systems are attracting attentions of the users. However, most of the systems have failed in properly reflecting users` characteristics and thus they have difficulty in recommending appropriate resources to users. In this paper, we propose an internet resource recommendation method using FOAF and SNA which fully reflects the characteristics of users. In our method, 1) we extract the data about user characteristics and tags using FOAF; 2) we generate graphs representing users, user characteristics and tags after inserting data into 3 matrixes and integrating them; 3) we recommend the appropriate internet resources after selecting common characteristics of the recommended items and Hot tags by analyzing social network. For verification of our proposed method, we implemented our method to establish and analyze an experimental social group. We verified through our experiments that the more users added in the social network, the higher quality of recommendation result we got than the item-based recommendation method. By using the suggested idea in this paper, we can make a more appropriate recommendation of resources to users while effectively retrieving explosively increasing internet resources.