Mar 14, 2024 · Artificial Intelligence

How JD Retail Boosted Online Recommendation Inference with Distributed Heterogeneous Computing

This article details JD Retail's ad‑tech team's deep‑compute optimizations—including a distributed graph‑based heterogeneous framework, GPU‑focused inference engine enhancements, TensorBatch request aggregation, deep‑learning compiler bucket pre‑compilation, asynchronous compilation, and multi‑stream GPU processing—to overcome high‑concurrency, low‑latency online recommendation challenges.

Deep Learning CompilerGPU inferencedistributed computing

0 likes · 14 min read

How JD Retail Boosted Online Recommendation Inference with Distributed Heterogeneous Computing

Alibaba Cloud Developer

Dec 12, 2018 · Artificial Intelligence

Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach

To address the pseudo-exposure problem that reduces click-through rates in mobile e-commerce recommendation, the authors model the task as a contextual multiple-play bandit, propose weighted sample and similarity-enhanced linear reward extensions, provide sublinear regret proofs, and demonstrate significant CTR gains on real Taobao data.

Bandit AlgorithmsCTR optimizationcontextual multi-play

0 likes · 30 min read

Tackling Pseudo-Exposure in Mobile E-Commerce: A Contextual Multiple-Play Bandit Approach