Deep extreme multi-label learning (XML) requires training deep architectures that can tag
\na data point with its most relevant subset of labels from an extremely large label set. XML
\napplications such as ad and product recommendation involve labels rarely seen during training
\nbut which nevertheless hold the key to recommendations that delight users. Effective utilization of label metadata and high quality predictions for rare labels at the scale of millions of
\nlabels are thus key challenges in contemporary
\nXML research. To address these, this paper develops the SiameseXML framework based on a
\nnovel probabilistic model that naturally motivates
\na modular approach melding Siamese architectures with high-capacity extreme classifiers, and
\na training pipeline that effortlessly scales to tasks
\nwith 100 million labels. SiameseXML offers predictions 2\u201313% more accurate than leading XML
\nmethods on public benchmark datasets, as well
\nas in live A\/B tests on the Bing search engine,
\nit offers significant gains in click-through-rates,
\ncoverage, revenue and other online metrics over
\nstate-of-the-art techniques currently in production. Code for SiameseXML is available at https:
\n\/\/github.com\/Extreme-classification\/siamesexml<\/p>\n","protected":false},"excerpt":{"rendered":"
Deep extreme multi-label learning (XML) requires training deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. XML applications such as ad and product recommendation involve labels rarely seen during training but which nevertheless hold the key to recommendations that delight users. Effective utilization 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