Social bookmarking systems have been growing significantly in recent years. They allow users to bookmark URLs using their own keywords, known as tags. Tags can be used later for searching and categorizing bookmarks. With the growth of social bookmarking systems, the need to automatically recommend tags increases. Among the most popular approaches for tag recommendation is collaborative filtering. In this work, we address two main limitations of collaborative filtering, the firsttime seen bookmarks that have not been tagged before and the cold-start users that have no sufficient history to use for recommendation. The focus of this work is to make social personalized tag recommendation for social bookmarking systems based on finding similar users and similar bookmarks. We proposed a new personalized tag recommendation system, PUT-Tag, that uses tag, user and content similarity in recommending tags. The experimental evaluation illustrated that the PUT-Tag algorithm outperforms benchmark approaches. Moreover, it achieves a similar performance when considering first time URLs for both active and cold-start users.
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