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| 标签推荐方法研究综述 |
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徐鹏宇1,2, 刘华锋1,2, 刘冰1,2, 景丽萍1,2, 于剑1,2
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1.交通数据分析与挖掘北京市重点实验室 北京交通大学, 北京 100044;2.北京交通大学 计算机与信息技术学院, 北京 100044
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| 摘要: |
| 随着互联网信息的爆炸式增长,标签(由用户指定用来描述项目的关键词)在互联网信息检索领域中变得越来越重要.为在线内容赋予合适的标签,有利于更高效的内容组织和内容消费.而标签推荐通过辅助用户进行打标签的操作,极大地提升了标签的质量,标签推荐也因此受到了研究者们的广泛关注.总结出标签推荐任务的三大特性,即项目内容的多样性、标签之间的相关性以及用户偏好的差异性.根据这些特性,将标签推荐方法划分为3个类别,分别是基于内容的方法、基于标签相关性的方法以及基于用户偏好的方法.之后,针对这3个类别下的对应方法进行了梳理和剖析.最后,提出了当前标签推荐领域面临的主要挑战,例如标签的长尾问题、用户偏好的动态性以及多模态信息的融合问题等,并对未来研究方向进行了展望. |
| 关键词: 机器学习 信息检索 推荐系统 标签推荐 用户偏好 |
| DOI：10.13328/j.cnki.jos.006481 |
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| 基金项目:国家自然科学基金(61773050) |
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| Survey of Tag Recommendation Methods |
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XU Peng-Yu1,2, LIU Hua-Feng1,2, LIU Bing1,2, JING Li-Ping1,2, YU Jian1,2
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1.Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University, Beijing 100044, China;2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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| Abstract: |
| 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. |
| Key words: machine learning information retrieval recommendation system tag recommendation user preference |
