NIS applies RL to jointly optimize per-feature vocabulary sizes and per-value embedding dimensions under memory constraint, reporting 6.8% Recall@1 and 1.8% ROC-AUC gains over manual baselines on retrieval and ranking tasks.
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Neural Input Search for Large Scale Recommendation Models
NIS applies RL to jointly optimize per-feature vocabulary sizes and per-value embedding dimensions under memory constraint, reporting 6.8% Recall@1 and 1.8% ROC-AUC gains over manual baselines on retrieval and ranking tasks.