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Instead of rebuilding the entire recommendation stack from scratch, LIGE-GR generalizes the existing pointwise recommendation system into a listwise generation system, which allows mature recommender systems to benefit from listwise optimization while preserving compatibility with existing models, value functions, and serving infrastructure.
Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.
Hard negatives generated in real-time by LLMs can significantly enhance the training of two-tower models, outperforming traditional sampling methods and reducing popularity bias in recommendations.
