With the explosive growth of Internet information, tags (keywords specified by users to describe the item) become more and more important in the field of Internet information retrieval. Giving appropriate tags to online content is conducive to more efficient content organization and content consumption. Tag recommendation greatly improves the quality of tags by assisting users to tag. Therefore, tag recommendation has been widely concerned by researchers. This study summarizes the three characteristics of tag recommendation task, that is, the diversity of item content, the correlation between tags, and the difference of user preferences. According to these three characteristics, tag recommendation methods are divided into three categories: content-based method, tag relevance based method, and user preference based method. After that, the corresponding methods are sorted out and analyzed under these three categories. Finally, the main challenges are presented in the field of tag recommendation, such as the long tail problem of tags, the dynamics of user preferences, and the fusion of multimodal information, and the future research is prospected as well.