ECLARE: Extreme Classification with Label Graph Correlations

Mittal, Anshul ; Sachdeva, Noveen ; Agrawal, Sheshansh ; Agarwal, Sumeet ; Kar, Purushottam ; Varma, Manik (2021) ECLARE: Extreme Classification with Label Graph Correlations In: WWW '21: Proceedings of the Web Conference 2021, April 2021, Ljubljana Slovenia.

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Official URL: http://doi.org/10.1145/3442381.3449815

Related URL: http://dx.doi.org/10.1145/3442381.3449815

Abstract

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 2–14% 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.

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to Association for Computing Machinery.
ID Code:119522
Deposited On:14 Jun 2021 06:46
Last Modified:14 Jun 2021 06:46

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