Build & Evaluate GDCN-Final Fusion Agent on Criteo
Gated Deep Cross Network (GDCN) enhances Click-Through Rate (CTR) prediction in recommender systems by improving interpretability. Implement the state-of-the-art GDCN-Final Fusion Agent architecture from scratch, leveraging its dual-gated GDCN stream, feature-selected MLP stream, and bilinear fusion. The challenge involves developing a robust data pipeline for the Criteo dataset, including log-binning for numerical features, training the model, and establishing a rigorous AUC evaluation harness. Practitioners will demonstrate their ability to translate a complex architectural description into a working deep learning model and rigorously assess its performance. This task simulates a real-world scenario where an ML engineer must reproduce a research paper's findings, ensuring all nuanced components are correctly implemented and evaluated on a large-scale industrial dataset. The focus is on correctness, efficiency, and achieving competitive AUC scores while maintaining a reproducible training and evaluation pipeline.
What you are building
The core problem, expected build, and operating context for this challenge.
Gated Deep Cross Network (GDCN) enhances Click-Through Rate (CTR) prediction in recommender systems by improving interpretability. Implement the state-of-the-art GDCN-Final Fusion Agent architecture from scratch, leveraging its dual-gated GDCN stream, feature-selected MLP stream, and bilinear fusion. The challenge involves developing a robust data pipeline for the Criteo dataset, including log-binning for numerical features, training the model, and establishing a rigorous AUC evaluation harness. Practitioners will demonstrate their ability to translate a complex architectural description into a working deep learning model and rigorously assess its performance. This task simulates a real-world scenario where an ML engineer must reproduce a research paper's findings, ensuring all nuanced components are correctly implemented and evaluated on a large-scale industrial dataset. The focus is on correctness, efficiency, and achieving competitive AUC scores while maintaining a reproducible training and evaluation pipeline.
Shared data for this challenge
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