Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

arXiv:2607.00448 · cs.IR, cs.AI · Submitted 2026-07-01 · Read on arXiv

cs.IR, cs.AI

Submitted: 2026-07-01

Updated: 2026-09-25

Terminology

• Clustering — 12× in this paper · explained in 2 episodes
• Approximate Nearest Neighbor Negative Contrastive Estimation — 3× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Global Out-of-Batch Sampling — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Queries Per Second — 1× in this paper
• Sparsity — 1× in this paper · explained in 4 episodes
• Vector Representations — 1× in this paper · explained in 2 episodes

Sources

• Managing Popularity Bias in Recommender Systems with Personalized Re-ranking
• Bias and Debias in Recommender System: A Survey and Future Directions
• Learning Dense Representations for Entity Retrieval
• Session-based Recommendations with Recurrent Neural Networks
• KuaiFormer: Transformer-Based Retrieval at Kuaishou
• Revisiting Neural Retrieval on Accelerators
• Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

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