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Recommendation Industries

Industry-specific recommendation implementations and use cases for various domains.

Overview​

The recommendation industries system provides specialized recommendation implementations tailored for specific industries and use cases.

Core Features​

  • E-commerce: Product recommendations, cross-selling, upselling
  • Media & Entertainment: Content recommendations, playlist generation
  • Finance: Investment recommendations, financial product suggestions
  • Healthcare: Treatment recommendations, drug suggestions
  • Education: Course recommendations, learning path suggestions

Usage Examples​

E-commerce Recommendations​

from recoagent.recommendations.industries import EcommerceRecommendationEngine

# Create e-commerce recommendation engine
ecommerce_engine = EcommerceRecommendationEngine(
    product_catalog=product_catalog,
    user_behavior_data=behavior_data
)

# Generate product recommendations
recommendations = ecommerce_engine.recommend_products(
    user_id="user_123",
    recommendation_type="collaborative_filtering",
    n_recommendations=10
)

# Cross-sell recommendations
cross_sell = ecommerce_engine.cross_sell_recommendations(
    product_id="product_456",
    n_recommendations=5
)

Media & Entertainment Recommendations​

from recoagent.recommendations.industries import MediaRecommendationEngine

# Create media recommendation engine
media_engine = MediaRecommendationEngine(
    content_library=content_library,
    user_preferences=preferences
)

# Generate content recommendations
content_recs = media_engine.recommend_content(
    user_id="user_123",
    content_type="movies",
    n_recommendations=20
)

# Generate playlist
playlist = media_engine.generate_playlist(
    user_id="user_123",
    mood="upbeat",
    duration_minutes=60
)

API Reference​

EcommerceRecommendationEngine Methods​

recommend_products(user_id: str, recommendation_type: str, n_recommendations: int) -> List[ProductRecommendation]​

Generate product recommendations

Parameters:

  • user_id (str): User identifier
  • recommendation_type (str): Type of recommendation
  • n_recommendations (int): Number of recommendations

Returns: List of product recommendations

MediaRecommendationEngine Methods​

recommend_content(user_id: str, content_type: str, n_recommendations: int) -> List[ContentRecommendation]​

Generate content recommendations

Parameters:

  • user_id (str): User identifier
  • content_type (str): Type of content
  • n_recommendations (int): Number of recommendations

Returns: List of content recommendations

See Also​