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Recommendation Agents

Agent-based recommendation systems providing intelligent and adaptive recommendation capabilities.

Overview​

The recommendation agents system provides intelligent agents that can learn, adapt, and provide personalized recommendations based on user behavior and preferences.

Core Features​

  • Intelligent Agents: AI-powered recommendation agents
  • Learning Capabilities: Continuous learning from user interactions
  • Personalization: User-specific recommendation strategies
  • Multi-Agent Systems: Collaborative recommendation agents
  • Real-time Adaptation: Dynamic recommendation updates

Usage Examples​

Basic Recommendation Agent​

from recoagent.recommendations.agents import RecommendationAgent

# Create recommendation agent
agent = RecommendationAgent(
    agent_type="collaborative_filtering",
    learning_rate=0.01,
    enable_online_learning=True
)

# Train agent
agent.train(training_data)

# Generate recommendations
recommendations = agent.recommend(
    user_id="user_123",
    n_recommendations=10
)

Advanced Multi-Agent System​

from recoagent.recommendations.agents import MultiAgentRecommendationSystem

# Create multi-agent system
multi_agent_system = MultiAgentRecommendationSystem(
    agents={
        "collaborative": CollaborativeAgent(),
        "content_based": ContentBasedAgent(),
        "hybrid": HybridAgent()
    },
    fusion_strategy="weighted_average"
)

# Generate recommendations using multiple agents
recommendations = multi_agent_system.recommend(
    user_id="user_123",
    context={"session_id": "session_456"}
)

API Reference​

RecommendationAgent Methods​

train(training_data: List[Dict]) -> None​

Train recommendation agent

Parameters:

  • training_data (List[Dict]): Training data

recommend(user_id: str, n_recommendations: int = 10) -> List[Recommendation]​

Generate recommendations

Parameters:

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

Returns: List of recommendations

See Also​