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Extreme multi-label classification (XML) involves tagging a data point withits most relevant subset of labels from an extremely large label set, withseveral applications such as product-to-product recommendation with millions ofproducts. Although leading XML algorithms scale to millions of labels, theylargely ignore label meta-data such as textual descriptions of the labels. Onthe other hand, classical techniques that can utilize label metadata viarepresentation learning using deep networks struggle in extreme settings. Thispaper develops the DECAF algorithm that addresses these challenges by learningmodels enriched by label metadata that jointly learn model parameters andfeature representations using deep networks and offer accurate classificationat the scale of millions of labels. DECAF makes specific contributions to modelarchitecture design, initialization, and training, enabling it to offer up to2-6% more accurate prediction than leading extreme classifiers on publiclyavailable benchmark product-to-product recommendation datasets, such asLF-AmazonTitles-1.3M. At the same time, DECAF was found to be up to 22x fasterat inference than leading deep extreme classifiers, which makes it suitable forreal-time applications that require predictions within a few milliseconds. Thecode for DECAF is available at the following URLhttps://github.com/Extreme-classification/DECAF.

DECAF: Deep Extreme Classification with Label Features