协同标签系统中现有的用户建模方法将用户视为标签向量，并假设向量中的标签均是用户感兴趣的，且只能分别计算单个标签之间的匹配程度，忽略了多个标签作为一个整体对用户兴趣产生的影响．为此，文中提出了一种基于标签组合效应的推荐算法( TGER) ．该算法利用用户对资源的评分筛选出对用户兴趣有重要影响的标签组合，通过高维标签组合优先匹配的方法计算用户与资源之间的相关度．在MovieLens 数据集上的实验结果显示，TGER算法能明显地提高推荐的质量．

In the existing user modeling methods for collaborative tagging systems,a user is regarded as a tag- vector and it is assumed to be interested in every tag in the tag- vector.Moreover,only the matching degree of a tag with another tag is calculated,while the effects of tags as a whole on the user’ s preference are ignored.In order to solve these problems,this paper proposes a recommendation algorithm based on the tag- group effect,namely,TGER.This algorithm utilizes the user ratings on resources to select the tag- groups which have significant effects on the user’ s preference,and adopts the high- dimension tag- group first matching method to calculate the user- resource relevance.Experimental results on the MovieLens data set show that TGER can significantly improve the recommendation qua-lity.