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