Scalability is a major challenge in modern recommender systems. In sequential recommendations, full Cross-Entropy (CE) loss achieves state-of-the-art recommendation quality but consumes excessive GPU memory with large item catalogs, limiting its practicality. Using a GPU-efficient locality-sensitive hashing-like algorithm for approximating large tensor of logits, this paper introduces a novel RECE (REduced Cross-Entropy) loss. RECE significantly reduces memory consumption while allowing one to enjoy the state-of-the-art performance of full CE loss. Experimental results on various datasets show that RECE cuts training peak memory usage by up to 12 times compared to existing methods while retaining or exceeding performance metrics of CE loss. The approach also opens up new possibilities for large-scale applications in other domains.

翻译：可扩展性是现代推荐系统面临的主要挑战。在序列推荐任务中，完整的交叉熵损失函数虽能实现最优的推荐质量，但在处理大规模物品目录时会消耗过多的GPU内存，从而限制了其实用性。本文通过采用一种基于GPU高效局部敏感哈希的算法来近似计算大型逻辑张量，提出了一种新颖的RECE（简化交叉熵）损失函数。RECE在显著降低内存消耗的同时，使模型能够保持与完整交叉熵损失相当的最优性能。在多个数据集上的实验结果表明，与现有方法相比，RECE将训练峰值内存使用量降低了最高达12倍，同时保持甚至超越了交叉熵损失的性能指标。该方法还为其他领域的大规模应用开辟了新的可能性。