Utilizing user tag-based interests in recommender systems for social resource sharing websites

This study examines the proposed system's performance using an experimental dataset collected from a social bookmarking website. The experimental results show that the hybridization of user's preferences with frequency, recency, and duration plays an important role, and provides better performances than traditional collaborative recommendation systems. The experimental results also reveal that the friend network information can successfully collaborate, thus improving the collaborative recommendation process.