  The propensity model introduced by Jain et al. 2016 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.
