Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval
The two-tower model is widely used in the retrieval stage of large-scale recommendation systems, where training typically relies on in-batch and/or out-of-batch negative sampling. These methods, however, tend to produce easy negatives that the model learns quickly and that provide little training signal. This paper proposes a self-supervised, cluster-based hard negative sampling technique that draws negatives from the same semantic cluster as the positive item; in our production deployment the clusters are derived from large language model (LLM) based multimodal content representations, so that intra-cluster items are genuinely similar and yield informative negatives. To make this deploya...
| Authors | Ivan Ji, Liuyi Hu, Harrison et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 July 2026 |
| Last updated at source | 25 September 2026 |
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