Optimizing JD Advertising Retrieval Platform: Balancing Compute, Data Scale, and Iterative Efficiency
The article details how JD's advertising retrieval platform tackles the core challenge of balancing limited compute resources with massive data by optimizing compute allocation, improving model scoring efficiency, and enhancing iteration speed through distributed execution graphs, adaptive algorithms, and platform‑level infrastructure improvements.