Tag-Aware Recommender Systems: A State-of-the-art Survey

Abstract

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.

19 pages, 3 figures

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Cited by in corpus (19)

• Recommender Systems
• Progresses and Challenges in Link Prediction
• Network-based recommendation algorithms: A review
• Recommender Systems for Online and Mobile Social Networks: A survey
• Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics
• Promoting cold-start items in recommender systems
• Personalized recommendation with corrected similarity
• A Personalized Recommender System for Pervasive Social Networks
• Diffusion-like recommendation with enhanced similarity of objects
• Information Filtering on Coupled Social Networks
• Gravity Effects on Information Filtering and Network Evolving
• A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
• PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks
• Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs
• Parallel and Distributed Collaborative Filtering: A Survey
• Information Filtering via Balanced Diffusion on Bipartite Networks
• Heterogeneity Involved Network-based Algorithm Leads to Accurate and Personalized Recommendations
• Consistence beats causality in recommender systems
• Conquering the rating bound problem in neighborhood-based collaborative filtering: a function recovery approach