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Restoring collaborative signals in generative recommendations can significantly boost accuracy without retraining models or sacrificing efficiency.
Internalizing user behavior directly into generative recommendation models can boost conversion rates by up to 4.4% without compromising model integrity.
Orthogonal constraints can rescue sparse embeddings in recommender systems from representation collapse, unlocking significant performance gains in large-scale industrial deployments.
