CS3: Efficient Online Capability Synergy for Two-Tower Recommendation

TL;DR

This paper introduces CS3, an online framework that enhances two-tower recommender models by improving their capacity and alignment without sacrificing real-time performance.

Contribution

The paper proposes a novel, plug-and-play framework with three mechanisms to strengthen two-tower models for online recommendation systems.

Findings

Achieves up to 8.36% revenue increase in large-scale deployment.

Demonstrates consistent improvements on three public datasets.

Maintains millisecond-level latency in real-time scenarios.

Abstract

To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the isolated two-tower architecture restricts representation capacity, embedding-space alignment, and cross-feature interactions. Existing solutions such as late interaction and knowledge distillation can mitigate these issues, but often increase latency or are difficult to deploy in online learning settings. We propose Capability Synergy (CS3), an efficient online framework that strengthens two-tower retrievers while preserving real-time constraints. CS3 introduces three mechanisms: (1) Cycle-Adaptive Structure for self-revision via adaptive feature denoising within each tower; (2) Cross-Tower Synchronization to improve alignment through lightweight mutual awareness between towers; and (3) Cascade-Model Sharing to enhance cross-stage consistency by reusing knowledge from downstream models. CS3 is plug-and-play with diverse two-tower backbones and compatible with online learning. Experiments on three public datasets show consistent gains over strong baselines, and deployment in a largescale advertising system yields up to 8.36% revenue improvement across three scenarios while maintaining ms-level latency.