Tag-Aware Recommender Systems: A State-of-the-art Survey
It provides a comprehensive overview for researchers and practitioners in recommender systems, but it is incremental as it synthesizes existing work without new results.
This survey summarizes recent progress in tag-aware recommender systems, covering network-based, tensor-based, and topic-based methods, and outlines future challenges.
In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.