How Large Language Models Are Transforming Health E‑Commerce Recommendations

This article explains how JD Health’s recommendation team integrates large‑model technologies—scaling CTR models, enhancing pipelines with LLMs, and adopting generative models—into e‑commerce recommendation systems, highlighting practical applications and technical challenges specific to the health‑commerce sector.

CTR modelsRecommendation Systemsai

0 likes · 5 min read

How Large Language Models Are Transforming Health E‑Commerce Recommendations

NewBeeNLP

Jul 5, 2024 · Artificial Intelligence

Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance

Meta’s new paper introduces the Wukong model, demonstrating that expanding dense‑layer parameters and computational FLOPs in large‑scale recommendation systems follows a clear scaling law, yielding consistent performance gains across massive internal datasets, with detailed analysis of feature modules, parameter impacts, and experimental results.

CTR modelsDeep LearningMeta

0 likes · 10 min read

Unveiling Meta’s Wukong: How Scaling Laws Boost Large‑Scale Recommendation Performance

DataFunTalk

Oct 12, 2022 · Artificial Intelligence

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

This article presents a comprehensive overview of feature embedding modeling in recommendation systems, discussing the necessity of feature modeling, three technical directions (gate threshold, variable‑length embeddings, and enrichment), detailed descriptions of models such as FiBiNet, FiBiNet++, ContextNet, and MaskNet, experimental findings, and a Q&A session that addresses practical challenges and future work.

CTR modelsRecommendation SystemsWeibo

0 likes · 34 min read

Feature Embedding Modeling for Recommendation Systems: Techniques, Models, and Practical Insights from Weibo

DataFunTalk

Aug 27, 2020 · Artificial Intelligence

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models

This article explains the fundamental differences between computational advertising and recommendation systems, outlines the distinct problems each field addresses, and surveys the most widely used advertising models—including traditional machine‑learning approaches, deep‑learning architectures, and hybrid solutions—providing practical insights for engineers in both domains.

CTR modelsDeep LearningRecommendation Systems

0 likes · 11 min read

Computational Advertising vs Recommendation Systems: Key Differences and Popular Models