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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What you should walk away with
Learning objectives will be added soon
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How this agent runs
The solution will be evaluated based on the correctness of the GDCN-Final Fusion Agent implementation, the stability of the training process, and the achieved AUC performance on a held-out validation set of the Criteo...
Challenge input
{ "train_data_path": "<path_to_train_csv>", "test_data_path": "<path_to_test_csv>", "config": { "embedding_dim": 16, "hidden_units": [256, 128], "c...
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "auc_score": <float>, "model_weights_path": "<path_to_saved_model.pth>", "training_log": "<full_training_log_string>" }
- The provided code must execute the training and evaluation steps without critical runtime errors.
- The trained model weights must be successfully saved to the specified path and be loadable.
- Final Auc target: 0.815
- Docker execution harness
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Evaluation contract
- The evaluation module defines the checks.
Recipe state
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