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This paper introduces TransRetrieval, a novel Transformer-based retrieval framework designed to address the challenges of feature heterogeneity in recommendation systems. By employing weighted average aggregation, target token compression, and position-style domain embeddings, the framework achieves significant improvements in recall and computational efficiency on large-scale datasets. The results demonstrate a robust log-linear scaling of recall metrics and a tangible increase in platform revenue, underscoring the practical impact of the proposed methods in industrial applications.
TransRetrieval boosts recommendation recall by over 19 points while slashing computational costs by 85%, proving that smarter aggregation can outperform brute force scaling.
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.
