摘要
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.
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 reconmaendation 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 au- tonomy oriented personalized tag recommendations. The parameters of the proposed method were estimated following a Gibbs sam- piing approach, which allowed a quick calculation of the values. Experiment results showed that the proposed approach can provide personalized tag recommendations with higher quality.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2012年第12期2353-2359,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61073062)
辽宁省自然科学基金(No.20102060)
中央高校基本科研业务费(No.N090604010)
