Empirical scaling of generative recommenders to 1B parameters shows task-dependent gains and requires targeted adaptations for production constraints like latency and item freshness.
CoST: Con- trastive quantization based semantic tokenization for generative recommendation
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Towards Generalizable and Efficient Large-Scale Generative Recommenders
Empirical scaling of generative recommenders to 1B parameters shows task-dependent gains and requires targeted adaptations for production constraints like latency and item freshness.