Tag Recommendation Based on Bayesian Principle
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.
Tag recommendation Bayesian principle Social tagging Algorithm
Zhonghui Wang Zhihong Deng
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
国际会议
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)
重庆
英文
191-201
2010-11-19(万方平台首次上网日期,不代表论文的发表时间)