Multi-Instance Multi-Label Learning For Automatic Tag Recommendation

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 associate 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 multi-label 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. The experiments on real-word data from delicious suggest that our framework has better performance than traditional multi-label learning methods on the task of tag recommendation.