Towards Eective Recommendation in a Social Annotation System Through Group Extraction
Yanhui Gu, Zhenglu Yang, Masaru Kitsuregawa · 2011
With the recent information explosion, social websites have become popular in many applications where abundant social data is available. Many social annotation services allow users to annotate various resources with tags, which can facilitate users finding preferred resources. However, in social annotation based recommendation researches, obtaining the proper relationship between user, resource and tag is still a challenge. In this paper, we judiciously extract anity relationship from between tags and resources and between tags and users. The key