【摘要】 Last.FM,MovieLens等在线服务一个重要的特征是用户能手动使用标签标注对象,很多研究人员使用标签改进推荐质量.但是,标签是一个用户对对象的局部描述,不同的用户可能使用不同的标签来描述同一个对象,但表达同样的意义,这导致了单纯的使用标签进行推荐所达到的效果不理想.因此,本文首先根据标签共生分布计算标签相似度,然后根据相似度分组同义标签,最后通过组标签计算用户的相似度,将其混合到经典的协同过滤推荐算法中.实验结果表明,该方法能有效地改进现有的推荐算法,达到更好的推荐效果.更多还原

【Abstract】 An important facet of Last.FM and MovieLens is that users manually annotate the items using so called tags.There are many researches about using tag to improve the quality of recommendation.However,tags are "local" descriptions of items given by the users,because different people use different tags for the same item,but the tags may represent the same means.In this paper,we used tag grouping method to group the tag according to the similarity of co-occurrence distributions.Based on this,we proposed an approach to group synonymy tags and fusing the relationship between users-tag with the collaboration filtering algorithms.The results of the empirical evaluation show that the approach is effectiveness in augmenting recommendation.更多还原