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

Jain, Himanshu ; Prabhu, Yashoteja ; Varma, Manik (2016) Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications In: KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, San Francisco, CA, USA.

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Official URL: http://doi.org/10.1145/2939672.2939756

Related URL: http://dx.doi.org/10.1145/2939672.2939756

Abstract

The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant subset of labels from an extremely large label set. Unfortunately, existing loss functions, such as the Hamming loss, are unsuitable for learning, model selection, hyperparameter tuning and performance evaluation. This paper addresses the issue by developing propensity scored losses which: (a) prioritize predicting the few relevant labels over the large number of irrelevant ones; (b) do not erroneously treat missing labels as irrelevant but instead provide unbiased estimates of the true loss function even when ground truth labels go missing under arbitrary probabilistic label noise models; and (c) promote the accurate prediction of infrequently occurring, hard to predict, but rewarding tail labels. Another contribution is the development of algorithms which efficiently scale to extremely large datasets with up to 9 million labels, 70 million points and 2 million dimensions and which give significant improvements over the state-of-the-art. This paper's results also apply to tagging, recommendation and ranking which are the motivating applications for extreme multi-label learning. They generalize previous attempts at deriving unbiased losses under the restrictive assumption that labels go missing uniformly at random from the ground truth. Furthermore, they provide a sound theoretical justification for popular label weighting heuristics used to recommend rare items. Finally, they demonstrate that the proposed contributions align with real world applications by achieving superior clickthrough rates on sponsored search advertising in Bing.

Item Type:Conference or Workshop Item (Paper)
Source:Copyright of this article belongs to Association for Computing Machinery.
ID Code:119559
Deposited On:14 Jun 2021 10:02
Last Modified:14 Jun 2021 10:02

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