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Serif Oyindamola Oyesiji

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Compressing Recommender and Ranking Models for Edge Deployment: A Survey

Edge deployment of recommender and ranking models enables low-latency personalization, privacy-preserving inference, reduced network dependence, and context-aware decisions on mobile, embedded, and resource-constrained platforms. Yet contemporary recommenders combine massive embedding tables, interaction networks, sequ...

Serif Oyindamola Oyesiji · 0 citations

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