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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.
A unified framework reveals that the choice of tokenization and vocabulary topology can significantly influence the performance of discrete diffusion models, unlocking new avenues for optimization.