Tag Recommendation Based on Bayesian Principle

摘要：

Social tagging systems have become increasingly a popular way to organize online heterogeneous resources. Tag recommendation is a key feature of social tagging systems. Many works has been done to solve this hard tag recommendation problem and has got same good results these years. Taking into account the complexity of the tagging actions, there still exist many limitations. In this paper, we propose a probabilistic model to solve this tag recommendation problem. The model is based on Bayesian principle, and its very robust and efficient. For evaluating our proposed method, we have conducted experiments on a real dataset extracted from BibSonomy, an online social bookmark and publication sharing system. Our performance study shows that our method achieves good performance when compared with classical approaches.

关键词： Tag recommendation Bayesian principle Social tagging Algorithm

作者: Zhonghui Wang Zhihong Deng

作者单位: Key Laboratory of Machine Perception (Ministry of Education),School of Electronics Engineering and C The State Key Lab of Computer Science, Institute of Software,Chinese Academy of Sciences, Beijing 10

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 191-201

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）