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中文摘要：
在过去的十年，社会标注系统吸引了增加注意从物理并且计算机科学社区。除标注系统的内在的结构和动力学以外，许多努力被探讨了统一标注信息揭示用户行为和偏爱，在项目之中提取潜伏的语义关系，等等做建议。明确地，这篇文章关于标签知道的 recommender 系统总结最近的进步，在从三个主流的观点和途径的贡献上强调：基于网络的方法，基于张肌的方法，和基于话题的方法。最后，我们构画出一些另外的标签相关的研究和标签知道的建议算法的未来挑战。
英文摘要：
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 studies and future challenges of tag-aware recommendation algorithms.
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