Abstract：Social tags are important styles of information organizing on the Web 2.0 era. Tag recommendation can help users collect, search and share online resources effectively. The previous approaches are focused on using single types of textual information, e.g. summary of a movie. But in practice there are various types of textual information that can be used for tag recommendation. For example, a movie contains both summary and comment information. Different types of information reflect different aspects of the movie. Thus we propose a novel approach to combine both summary and comment information to recommend tags. Furthermore, we use different ensemble learning approaches to incorporate the above information. The experimental results show that our proposed approach using different types of information outperform using single types of textual information in the tag recommendation tasks. Key words natural language processing; social tags; ensemble learning
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2015, Vol. 29 
