Extreme multi-label classification is an important data mining technique, which can be used to label each unseen instance with a subset of labels from a large label set. It has wide applications and many methods have been proposed in recent years. Existing methods either seek to compress label space or train a classifier based on instances' features, among which tree-based classifiers enjoy the advantages of better efficiency and accuracy. In many real world applications, instances are not independent and relationship between instances is very useful information. However, how to utilize relationship between instances in extreme multi-label classification is less studied. Exploiting such relationship may help improve prediction accuracy, especially in the circumstance that feature space is very sparse. In this paper, we study how to utilize the similarity between instances to build more accurate tree-based extreme multi-label classifiers. To this end, we introduce the utilization of relationship between instances to state-of-the-art models in two ways: feature engineering and collaborative labeling. Extensive experiments conducted on three real world datasets demonstrate that our proposed method achieves higher accuracy than the state-of-the-art models.
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