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Co-training item embeddings in recommendation systems can boost prediction accuracy and engagement while slashing computational costs by 30%.
Achieve zero-collision embedding tables in production recommenders without sacrificing training speed, unlocking better personalization via fresher and higher-quality item embeddings.
