---

name: recommenders description: "Use Microsoft Recommenders to prepare recommendation data, train and choose recommender models, evaluate offline metrics, tune experiments, and plan optional Spark/GPU/cloud workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Microsoft Recommenders

Use this repo skill when a task names recommenders, Microsoft/Recommenders, or needs package-specific guidance for recommendation-system data preparation, model selection/training/scoring, offline metrics, tuning, benchmarks, or operationalization patterns.

Backend truth for this skill

• Verified in the base CPU scope: package import, pandas data utilities/splitters, Python evaluation metrics, SAR, TF-IDF utilities, Cornac/LightGBM helper imports, parameter sweep, K8s sizing utilities, and bundled tiny smoke scripts.
• Optional and not verified in this CPU scope: Spark/PySpark workflows, TensorFlow/PyTorch deep-learning/GPU models, AzureML, Databricks, AKS, SARplus, and experimental dependencies.
• A visible GPU or Spark host is not enough. Verify the matching package extra, framework, system runtime, data, and credentials before claiming backend coverage.

Installation quick start

For ordinary CPU package use:

pip install recommenders
python - <<'PY'
import recommenders
print(recommenders.__version__)
PY

Optional workflow families use documented extras such as recommenders[gpu], recommenders[spark], or recommenders[experimental]. Install only the extra required by the selected workflow; avoid broad [all] installs for small tasks.

Route map

• data-preparation: load or validate interaction data, split train/test data, sample negatives, convert sparse/LibFFM formats, and handle dataset download/cache issues.
• modeling: choose model families, instantiate, fit, score, and recommend with SAR, TF-IDF, Cornac, LightGBM helpers, optional Spark/deep-learning/news/sequential/experimental models, and run tiny model smokes.
• evaluation: compute rating, ranking, diversity, novelty, serendipity, and optional Spark metrics; fix column/type/top-k metric failures.
• operations-and-tuning: plan parameter sweeps, NNI/AzureML tuning, Databricks/AKS operationalization, benchmark comparisons, and environment/backend readiness.

Shared references and scripts

• Read package-overview.md for module families, extras, and workflow-to-sub-skill mapping.
• Read troubleshooting.md for cross-cutting install/import, optional dependency, backend, cloud, and data-download failures.
• Read repo-provenance.md before deciding whether this skill is stale for a different checkout or package version.
• Run check_recommenders_environment.py to check base imports and optionally run all bundled CPU smoke helpers from an installed skill tree.

Suggested workflow order

1. Start with data-preparation to validate columns, split strategy, and candidate generation.
2. Use modeling to choose the smallest model family that matches the data and backend.
3. Use evaluation to select metrics and validate prediction dataframe contracts.
4. Use operations-and-tuning only when the user needs tuning, benchmarks, deployment, or backend readiness beyond a local CPU smoke.

Safety boundaries

• Do not run dataset downloads, notebooks, cloud scripts, Spark jobs, GPU training, benchmark loops, or cluster mutations unless the user explicitly authorizes the needed network, credentials, compute, and time budget.
• Do not treat skipped optional native cases as passing. Report them as optional or unverified.
• Runtime instructions in this skill are self-contained; do not ask a future agent to open the original source checkout for normal package usage.