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skills/recommendation-engine/assets/README.md

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+# Assets
+
+Bundled resources for recommendation-engine skill
+
+- [ ] example_user_data.csv: Example user data for testing the recommendation engine.
+- [ ] example_item_data.csv: Example item data for testing the recommendation engine.
+- [ ] recommender_template.py: Template for creating a new recommendation engine.
+- [ ] configuration_template.yaml: Template for configuring the recommendation engine.

skills/recommendation-engine/assets/configuration_template.yaml

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+# Configuration template for the recommendation engine plugin.
+
+# Data source configuration
+data_source:
+  type: "csv"  # Type of data source: csv, database, api, etc.
+  path: "data/user_item_interactions.csv" # Path to the data file or API endpoint
+  # Database connection details (if applicable)
+  database:
+    host: "localhost" # REPLACE_ME: Database host
+    port: 5432       # Database port
+    name: "recommendations_db" # REPLACE_ME: Database name
+    user: "recommendations_user" # REPLACE_ME: Database user
+    password: "YOUR_DATABASE_PASSWORD" # REPLACE_ME: Database password
+
+# Feature engineering configuration
+feature_engineering:
+  user_features:
+    - "age"       # List of user features to use
+    - "location"
+    - "interests"
+  item_features:
+    - "category"    # List of item features to use
+    - "price"
+    - "description"
+  interaction_features:
+    - "rating"    # List of interaction features to use
+    - "timestamp"
+  feature_scaling: "standard" # Feature scaling method: standard, minmax, none
+
+# Model configuration
+model:
+  type: "collaborative_filtering" # Type of recommendation model: collaborative_filtering, content_based, hybrid
+  algorithm: "als" # Algorithm to use for the model: als, sgd, knn, etc.
+  hyperparameters:
+    n_factors: 50  # Number of latent factors in collaborative filtering
+    reg_param: 0.01 # Regularization parameter
+    epochs: 10      # Number of training epochs
+  cold_start_strategy: "popularity" # Strategy for handling cold start problems: popularity, average, etc.
+
+# Evaluation configuration
+evaluation:
+  metrics:
+    - "precision"   # List of evaluation metrics to use
+    - "recall"
+    - "ndcg"
+  split_ratio: 0.8  # Ratio of data to use for training (remaining for testing)
+  k: 10           # Number of recommendations to consider for evaluation
+
+# Deployment configuration
+deployment:
+  model_path: "models/recommendation_model.pkl" # Path to save the trained model
+  api_endpoint: "/recommendations" # API endpoint for serving recommendations
+  num_recommendations: 5  # Default number of recommendations to return
+
+# Logging configuration
+logging:
+  level: "INFO"   # Logging level: DEBUG, INFO, WARNING, ERROR, CRITICAL
+  file: "logs/recommendation_engine.log" # Path to the log file
+
+# Performance optimization configuration
+optimization:
+  enable_caching: true # Enable caching of recommendations
+  cache_expiry: 3600  # Cache expiry time in seconds (1 hour)
+
+# Error handling configuration
+error_handling:
+  retries: 3       # Number of retries for failed requests
+  retry_delay: 60   # Delay between retries in seconds