Deep extreme classification (XC) seeks to train deep architectures that can
tag a data point with its most relevant subset of labels from an extremely
large label set. The core utility of XC comes from predicting labels that are
rarely seen during training. Such rare labels hold the key to personalized
recommendations that can delight and surprise a user. However, the large number
of rare labels and small amount of training data per rare label offer
significant statistical and computational challenges. State-of-the-art deep XC
methods attempt to remedy this by incorporating textual descriptions of labels
but do not adequately address the problem. This paper presents ECLARE, a
scalable deep learning architecture that incorporates not only label text, but
also label correlations, to offer accurate real-time predictions within a few
milliseconds. Core contributions of ECLARE include a frugal architecture and
scalable techniques to train deep models along with label correlation graphs at
the scale of millions of labels. In particular, ECLARE offers predictions that
are 2 to 14% more accurate on both publicly available benchmark datasets as
well as proprietary datasets for a related products recommendation task sourced
from the Bing search engine. Code for ECLARE is available at
this https URL

ECLARE 是一种深度极端分类的算法，通过引入标签相关性来提高分类准确性，从而实现对于海量标签的实时预测。该算法在公共数据库和相关产品推荐数据上都表现良好。