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• [P471] Multi-Instance Multi-Label Learning For Automatic Tag Recommendation
  Author(s): Chen Shen, Jun Jiao, Bin Wang, Yahui Yang
  
  

  Abstract
  Tag services have recently become one of the most popular Internet services on the World Wide Web. Due to the fact that a web page can be associated with multiple tags, previous research on tag recommendation mainly focuses on improving its accuracy or efficiency through multi-label learning algorithms. However, as a web page can also be split into multiple sections and be represented as a bag of instances, multi-instance multilabel learning framework should fit this problem better. In this paper, we improve the performance of tag suggestion by using multi-instance multi-label learning. Each web page is divided into a bag of instances. Then,. Experiments on real-word data from Del.icio.us suggest that our framework has better performance than traditional multi-label learning methods on the task of tag recommendation.

  
   

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