A paper proposes self-supervised hard-negative sampling for two-tower recommendation, using LLM-derived item clusters to generate harder examples in real time.
The paper proposes a self-supervised hard-negative sampling technique for two-tower recommendation models. It uses LLM-derived item clusters to generate harder negatives in real time. The aim is to address a limitation of standard negative sampling in large-scale retrieval; the summary gives no metrics or quantitative results.
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