CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems

1 code implementation • 5 Aug 2022 • Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu, Yu Zhang

In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.

Recommendation Systems Retrieval