"Improving feature interactions at Pinterest under industry constraints"

Pinterest's research tackles feature interaction challenges in recommendation systems under real-world constraints, improving their Homefeed ranking model while balancing performance with practical limitations like memory, latency, and reproducibility.

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https://arxiv.org/abs/2412.01985v1

🔍 Original Problem:

→ While academic research proposes advanced feature interaction techniques for recommendation systems, industrial implementation faces unique constraints like model latency, GPU memory limits, and reproducibility requirements.

→ Direct application of state-of-the-art architectures often proves impractical in production environments.

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🛠️ Solution in this Paper:

→ The team developed a hybrid architecture combining parallel MaskNet layers with stacked DCNv2 layers.

→ They implemented input transformation techniques and optimized hyperparameters within memory constraints.

→ The solution maintains reproducibility while achieving higher-order feature interactions.

→ Memory consumption increased by only 5% absolute, allowing same batch size usage.

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💡 Key Insights:

→ Higher-order feature interactions significantly improve model performance

→ Parallel feature interaction layers outperform sequential ones

→ Non-linear interactions boost model quality

→ Memory constraints heavily influence architecture choices

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📊 Results:

→ Homefeed Save Volume increased by 1.42%

→ Overall Time Spent improved by 0.39%

→ Zero latency increase in final implementation

→ Maintained model stability and reproducibility