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name: modeling description: "Choose, instantiate, fit, score, and recommend with Microsoft Recommenders model families across verified CPU and optional Spark, GPU, and experimental backends." disable-model-invocation: true metadata: disco-role: operating license: MIT

Modeling

Use this sub-skill when a user asks which Recommenders model family to use, how to instantiate and fit a model, how to produce predictions or top-k recommendations, or how optional Spark/deep-learning/content-based/experimental model paths differ.

Backend truth for this skill

• Verified in the base CPU scope: package import, SAR, TF-IDF utilities, Cornac prediction helpers, and LightGBM helper imports/signatures.
• Optional and not verified in this CPU scope: Spark ALS/SARplus/Spark LightGBM, TensorFlow/PyTorch deep-learning models, NewsRec/DeepRec notebooks, cloud workflows, and experimental packages such as Surprise, LightFM, Vowpal Wabbit, xLearn, GeoIMC, and RLRMC dependencies.
• Do not present a visible GPU, Spark, or cloud host as sufficient. The matching optional Python packages, system runtime, data files, and credentials must also be present.

Start here

• For algorithm choice across SAR, TF-IDF, Cornac, LightGBM, Spark ALS, NewsRec, NCF, Wide&Deep, VAE, RBM, SASRec/SSEPT, and experimental families, read model-overview.md.
• For constructors, methods, dataframe contracts, and return columns, read api-reference.md.
• For runnable recipes, use workflows.md:
  • CPU SAR collaborative filtering with SAR.fit, SAR.predict, and SAR.recommend_k_items.
  • CPU TF-IDF content-based recommendation with TfidfRecommender.
  • Cornac scoring/ranking helpers.
  • LightGBM feature encoding helper usage.
  • Optional Spark, deep-learning, and news/sequential model preparation checklists.
• For symptoms and fixes, read troubleshooting.md.
• To prove a local install can run the two lightweight CPU model paths, run:

python sub-skills/modeling/scripts/sar_tiny_smoke.py --top-k 2
python sub-skills/modeling/scripts/tfidf_tiny_smoke.py --top-k 1

Run the commands from the root of this generated skill tree, or adapt the script paths to the location where the skill was installed.

Route elsewhere

• Data loading, schema validation, de-duplication before training, filtering, negative sampling, sparse matrix conversion, and train/test splitting belong in data-preparation.
• Metric calculation after predictions or top-k output belongs in evaluation.
• Hyperparameter sweeps, benchmark harnesses, NNI/AzureML/Databricks/AKS, service sizing, and long-running operationalization belong in operations-and-tuning.

Working rules

1. Identify the available data signal first: interaction ratings/clicks, item text/content, tabular ad features, sequence/session logs, news/MIND files, or Spark-scale data.
2. Identify the permitted backend: base CPU only, Spark, GPU/deep-learning, experimental native packages, or cloud services.
3. Pick the smallest verified path that matches the data. Prefer SAR for a lightweight pandas interaction baseline and TF-IDF for text-only item similarity before escalating to optional models.
4. Keep userID, itemID, rating, timestamp, and prediction column names unless the user explicitly uses custom names and passes matching col_* parameters.
5. When producing recommendations for evaluation, ensure top-k output has one row per (userID, itemID) candidate and a prediction score, then route metrics to the evaluation sub-skill.