Updateable Embedding Caches And Freshness-Aware Retrieval In Large-Scale Recommendation Systems

Abstract

Large-scale recommendation systems rely heavily on embedding-based retrieval to efficiently identify relevant candidates from massive content inventories. While embeddings enable scalable similarity search, their effectiveness is tightly coupled to the freshness of underlying representations. In dynamic content ecosystems, stale embeddings degrade retrieval quality, delay exposure of newly introduced items, and exacerbate cold-start effects.

Traditional static embedding stores offer operational simplicity and predictable performance but impose significant delays in incorporating new information. This paper introduces updateable embedding caches as a system design pattern that bridges the gap between efficiency and adaptability in large-scale retrieval systems. By enabling incremental updates to embedding representations, these systems reduce freshness latency while preserving serving performance.

We examine the architectural trade-offs, including indexing strategies, consistency models, and memory efficiency considerations required to support dynamic embedding updates. Furthermore, we argue that cold-start behavior should be viewed not only as a statistical limitation but also as a consequence of infrastructure design decisions. Through this lens, updateable embedding caches emerge as a critical component for modern recommendation systems, enabling faster adaptation to evolving content and user behavior while maintaining scalable retrieval performance.