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name: deepctr description: "Use this DeepCTR repo skill for CTR/recommender feature columns, Keras models, sequence/session models, multitask models, and legacy TensorFlow Estimator workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepCTR

Use this repo skill when the task mentions DeepCTR, click-through-rate prediction, recommender models, sparse/dense feature columns, DIN/BST/DIEN/DSIN, multitask heads, or the legacy TensorFlow Estimator surface.

Install and verify

DeepCTR does not install TensorFlow for you. Install a TensorFlow build that matches your Python and platform, then install DeepCTR:

python -m pip install "numpy<2" "tensorflow<2.21"
python -m pip install deepctr

If you are using a TensorFlow 2.20-style stack with legacy Keras requirements, install the matching tf-keras package and set TF_USE_LEGACY_KERAS=1 before running DeepCTR. See references/installation-and-compatibility.md for compatibility notes.

Quick environment check:

python scripts/check_deepctr_env.py --json

Route by need

• data-and-feature-columns for SparseFeat, DenseFeat, VarLenSparseFeat, hashing, vocabulary paths, and input schema validation.
• keras-model-workflows for ordinary CTR/regression models, compile/fit/predict/save/load, and model selection.
• sequence-models for DIN, BST, DIEN, DSIN, hist_/sess_ naming, and sequence/session debugging.
• multitask-models for SharedBottom, ESMM, MMOE, PLE, and multi-output target packing.
• estimator-workflows for tf.estimator, TFRecord/Pandas input functions, and runtime gating.

Read first

• references/troubleshooting.md for cross-cutting install/import/version/GPU issues.
• references/repo-provenance.md when you need to check freshness or decide whether this skill is stale.

Common starting point

If the user has not specified a model family yet, start with data-and-feature-columns when the problem is input shaping, otherwise keras-model-workflows for single-output model choice.

What this skill does not do

• It does not tell you to open the original repository's examples or tests at runtime.
• It does not require a GPU unless the user explicitly asks for GPU or multi-GPU execution.
• It does not require Estimator workflows when tf.estimator is unavailable; in that case, use the Keras routes.