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arXiv · 2608.25528

TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation

Zhifei Zheng·Yunfei Liu·Bin Liu·Qiren Zhu·Hanbing Liu·Ziru Xu·Han Zhu·Jian Xu·Qi Qi·Bo Zheng
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Abstract

Applying scaling laws to recommendation retrieval is hindered by feature heterogeneity: naively stacking Transformer layers yields diminishing returns because heterogeneous fields produce severe token-norm divergence. We present TransRetrieval, a Transformer-based retrieval framework that scales with both computational budget and cross-domain data. The key enabler is (1) weighted average aggregation, which restores the homogeneous-token assumption Transformers rely on. Building on this, we introduce (2) target token compression that cuts per-candidate FLOPs by 85% while preserving cross-attention expressiveness, and (3) position-style domain embeddings that unify multiple domains at negligible additional cost, turning cross-domain data into a scaling asset. On a 40-billion-interaction industrial dataset and the public KuaiRand benchmark, scaling compute from 0.1 to 2 MFLOPs per target yields +19.3/+22.2 pt Recall@2000, confirming robust log-linear scaling. In online A/B tests, TransRetrieval lifts platform revenue by 2.53% under the same end-to-end latency constraint as the production baseline.

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Zhifei Zheng, Yunfei Liu, Bin Liu, Qiren Zhu, Hanbing Liu, Ziru Xu, Han Zhu, Jian Xu, Qi Qi, Bo Zheng. 2026-08-26. TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation. https://doi.org/10.1145/3799682.3840118

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