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...