KDD Papers

AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification

Yukihiro Tagami (Yahoo Japan Corporation)


Abstract

Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called AnnexML. At training step, AnnexML constructs k-nearest neighbor graph of the label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method which efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show our AnnexML can significantly improve prediction accuracy, especially on data sets that have larger label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, which is a state-of-the-art embedding-based method.


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