Tagging is one of the most important ways to categorize or indexing information at the age of Web 2.0. To handle the disadvantages of tagging systems such as inconsistentcies, redundancy and incompleteness, tag recommendation methods improve the quality of tags by providing candidate tags. In order to further improve the quality of tag recommendations, a tag recommendation method is proposed which bases on a combined analysis of the relations of objects in a tagging system and the content of resources. An LDA based generative tagging system model TSM/Forc that models object relation and resource content in a combined way is introduced, together with a probabilistic based tag recommendation method and a Gibbs sampling based model parameter estimation approach. Experiment results show that the proposed method could provide more accurate recommendations than the latest methods.%标签是Web 2.0时代信息分类与索引的重要方式.为解决标签系统所面临的不一致性、冗余性以及完备性等问题,标签推荐通过提供备选标签的方法来提高标签的质量.为了进一步提升标签推荐的质量,提出了一种基于标签系统中对象间关系与资源内容融合分析的标签推荐方法,给出了基于LDA(latent Dirichlet allocation)的融合表示对象间关系与资源内容的标签系统生成模型TSM/Forc,提出了一种基于概率的标签推荐方法,并给出了基于吉布斯(Gibbs)抽样的参数估计方法.实验结果表明,该方法可以提供比当前主流与最新方法更加准确的推荐结果.
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