Scaling LLM-Enhanced Linear Autoencoders to Industrial-Size Catalogs

Abstract

LLM-enhanced linear autoencoders (L3AE) incorporate semantic item representations from large language models into collaborative filtering and demonstrate significant gains on long-tail items. However, L3AE requires dense n × n matrices, which makes it impractical for large catalogs — exactly where semantic enrichment would help most. We propose scalable formulations that factor the semantic similarity matrix into a compact low-rank form and compute all required precision-matrix products without ever building any dense n × n matrix. Our method preserves the algebraic structure of L3AE while replacing its dense collaborative precision with SANSA’s sparse approximate inverse, enabling efficient scaling to industrial-size catalogs. Experiments on several public datasets show consistent gains over multiple baselines, and these gains tend to grow in colder, sparser catalogs — a pattern consistent with semantic enrichment helping most where collaborative signals are weakest. Code and configs are available at https://github.com/ViV99/scal3ae.