Title: RETRIEVALFORMER: TRANSFORMER-QUALITY RECOMMENDATIONS WITH EFFICIENT ANN RETRIEVAL AND COLD-START RESILIENCE
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: recommendation systems, two-tower models, transformers, user modeling, neural networks, deep learning, information retrieval

Abstract: We propose RetrievalFormer, a novel two-tower neural recommender architecture that bridges the gap between transformer accuracy and retrieval efficiency
for sequential recommendation. RetrievalFormer employs a transformer encoder
to model user interaction sequences while using a lightweight item tower to encode items from their content features, enabling efficient approximate nearest
neighbor (ANN) retrieval at serving time. The key innovation is an attentionbased heterogeneous feature encoder that enriches both user and item representations by learning to weight and combine different feature modalities. By sharing
embedding tables across towers and leveraging feature-rich representations, our
model achieves three critical capabilities: (1) transformer-level recommendation
accuracy while avoiding expensive full-catalog softmax computation, (2) immediate recommendation of new items without retraining, and (3) dramatic inference speedup through ANN search. On standard benchmarks (Amazon Beauty,
Amazon Toys & Games, MovieLens-1M), RetrievalFormer achieves competitive
performance, reaching 86-91% of established transformer baselines’ Recall@20
while delivering up to 288× speedup at inference for large catalogs. In cold-start
experiments with held-out items, RetrievalFormer successfully recommends completely unseen items while baseline models fail entirely. Our approach enables
practical deployment of efficient recommendations at scale, offering a compelling
trade-off between model accuracy and serving efficiency.

TL;DR: A flexible two-tower architecture that combines transformer-based user modeling with efficient retrieval, matching state-of-the-art performance while maintaining sub-millisecond inference latency.