  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.
