面向自主意识的标签个性化推荐方法研究

摘要

In a social tagging system,a user's tagging habits,including choosing which resource to tag and using which tag to annotate a resource,are affected by one's own autonomy. Available personalized rag recommendation methods lack the ability to model such autonomy information, and limit the performance of these methods. This paper proposed a latent Dirichlet allocation like probabilistic approach,which modeled user autonomy information such as one's preferences on tag and resource use,to provide autonomy oriented personalized tag recommendations.The parameters of the proposed method were estimated following a Gibbs sampling approach,which allowed a quick calculation of the values.Experiment results showed that the proposed approach can provide personalized tag recommendations with higher quality.%在标签系统中,用户使用资源以及标签的习惯受到自身自主意识的影响.当前的标签个性化推荐方法缺乏对此类自主意识信息的描述,限制了个性化推荐的效果.通过采用类似LDA的概率模型,建模了用户的资源使用以及标签使用两方面的自主意识信息,实现了面向用户自主意识的标签推荐.模型的参数使用基于吉布斯抽样的方法进行估计,为快速高效计算模型参数提供了可能.实验结果显示该方法可以提供更高质量的标签个性化推荐结果.