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Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications.

, , and . KDD, page 935-944. ACM, (2016)

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DSFormer: Effective Compression of Text-Transformers by Dense-Sparse Weight Factorization., , and . CoRR, (2023)Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages., , , and . WWW, page 13-24. International World Wide Web Conferences Steering Committee / ACM, (2013)Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising., , , , and . WWW, page 993-1002. ACM, (2018)Extreme Regression for Dynamic Search Advertising., , , and . WSDM, page 456-464. ACM, (2020)Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems., , , and . CoRR, (2022)Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications., , and . KDD, page 935-944. ACM, (2016)FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning., and . KDD, page 263-272. ACM, (2014)Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation., , , , , , and . WSDM, page 441-449. ACM, (2018)Generalized Zero-Shot Extreme Multi-label Learning., , , , and . KDD, page 527-535. ACM, (2021)