关键词: Folksonomy标签推荐系统, 标签模糊, 标签冗余, 核K-means聚类

Abstract: Ambiguity of tag may give a false impression of success when the recommended tags offer little utility. Redundancy of tag can hamper the effort to judge recommendations as well. When using K-Means clustering to deal with this problem, the extraction of ambiguity tags and redundancy tags was inaccurate because the clustering effect was ineffective. Therefore, the K-Means clustering of kernel algorithm was used to deal with the problem of ambiguity and redundancy on tags. This approach improved the clustering effect because it could identify, extract and enlarge useful features of the sample by non-linear mapping. The experimental results show that, the K-Means clustering of kernel algorithm has better performance in the clustering of tag and resource, and the extraction of ambiguity tag and redundancy tag is more accurate.

Key words: tag recommendation system for Folksonomy, tag ambiguity, tag redundancy, K-Means clustering of kernel