Link Strength-Based Collaborative Filtering for Enhancing Prediction Accuracy

User-based collaborative filtering recommends items to users by analyzing user preferences. Nearest neighbors are identified based on similarity between users and preference prediction of items is performed by using the nearest neighbors. The prediction accuracy depends on how the nearest neighbors are identified among users. In this paper, we propose link strength-based user modeling by applying trust information between users and item ratings to enhance the prediction accuracy. In the proposed user modeling, nearest neighbor candidate is extracted in traditional manner and final nearest neighbor is identified by calculating user ranking with trust information. Trust information between users is presented by link and consists of direct and indirect relation. We evaluate the prediction accuracy on recommended items and experimental results show that the prediction accuracy is improved by applying the proposed method.