On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ D ] doktorant | [ P ] pracownik
2022
rozdział w monografii naukowej / referat
angielski
- extreme classification
- multi-label classification
- propensity model
- missing labels
- long-tail labels
- recommendation
PL The propensity model introduced by Jain et al has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this approach showing that despite its theoretical soundness, its application in contemporary XMLC works is debatable. We exhaustively discuss the flaws of the propensity-based approach, and present several recipes, some of them related to solutions used in search engines and recommender systems, that we believe constitute promising alternatives to be followed in XMLC.
14.08.2022
1547 - 1557
copyright
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