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RankDistil: Distillation for Ranking

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Knowledge distillation is an approach to improve the performance of a student model by using the knowledge of a complex teacher. Despite its success in several deep learning applications, the study of distillation is mostly confined to classification settings. In particular, the use of distillation in top-k ranking settings, where the goal is to rank k most relevant items correctly, remains largely unexplored. In this paper, we study such ranking problems through the lens of distillation. We present a framework for distillation for top-k ranking and establish connections with the existing ranking methods. The core idea of this framework is to preserve the ranking at the top by matching the k largest scores of student and teacher while penalizing large scores for items ranked low by the teacher. Building on our framework, we develop a novel distillation approach, RankDistil, specifically catered towards ranking problems with a large number of items to rank. Finally, we conduct experiments which demonstrate that RankDistil yields benefits over commonly used baselines for ranking problems.

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