# MemberJunction AI Recommendations

This directory contains the recommendation system framework for MemberJunction, enabling intelligent content and item recommendations based on user behavior, preferences, and content analysis.

## Overview

The Recommendations framework provides a flexible, provider-agnostic system for generating personalized recommendations. It supports multiple recommendation strategies and can integrate with various recommendation engines while maintaining a consistent API.

## Package Structure

- **[@memberjunction/ai-recommendations](./Engine)** - Core recommendation engine framework
  - Base classes for recommendation providers
  - Common interfaces and types
  - Request/response handling
  - Provider registration system
  - Integration with MemberJunction entities

## Architecture

### Core Components

1. **RecommendationEngineBase** - Singleton engine that manages providers
   - Provider discovery and registration
   - Request routing
   - Result aggregation
   - Caching layer

2. **RecommendationProviderBase** - Abstract base for providers
   - Standard interface for all recommendation providers
   - Configuration management
   - Error handling
   - Logging integration

3. **Recommendation Entities** - Database-backed configuration
   - Recommendation providers
   - Recommendation runs
   - User preferences
   - Item metadata

### Data Flow

```
User Request → Recommendation Engine → Provider Selection → Provider API
      ↓                                        ↓                ↓
   Context                               Configuration      External Service
      ↓                                        ↓                ↓
   Results ← Score/Rank ← Provider Response ← Raw Recommendations
```

## Key Features

### Flexible Provider System
- Support multiple recommendation algorithms
- Switch providers without code changes
- A/B testing different approaches
- Fallback providers for reliability

### Rich Context Support
- User preferences and history
- Item metadata and categories
- Contextual factors (time, location, device)
- Business rules and constraints

### Integration Points
- MemberJunction entity system
- User profiles and permissions
- Content management
- Analytics and tracking

## Getting Started

### Basic Usage

```typescript
import { RecommendationEngineBase } from '@memberjunction/ai-recommendations';

// Get engine instance
const engine = RecommendationEngineBase.Instance;

// Request recommendations
const recommendations = await engine.GetRecommendations({
    userId: 'user-123',
    itemType: 'Articles',
    count: 10,
    context: {
        currentArticleId: 'article-456',
        userInterests: ['technology', 'ai']
    }
});
```

### Provider Implementation

```typescript
import { RecommendationProviderBase } from '@memberjunction/ai-recommendations';
import { RegisterClass } from '@memberjunction/global';

@RegisterClass(RecommendationProviderBase, 'MyRecommendationProvider')
export class MyRecommendationProvider extends RecommendationProviderBase {
    async GetRecommendations(request: RecommendationRequest): Promise<RecommendationResult> {
        // Your recommendation logic here
        const items = await this.calculateRecommendations(request);
        
        return {
            success: true,
            recommendations: items,
            provider: this.Name,
            executionTime: Date.now() - start
        };
    }
}
```

## Recommendation Strategies

### Content-Based Filtering
Recommend items similar to what users have liked:
- Analyze item features
- Find similar items
- Consider user preferences
- Weight by interaction history

### Collaborative Filtering
Recommend based on similar users' preferences:
- User-item interaction matrix
- Find similar users
- Recommend their preferred items
- Handle cold start problem

### Hybrid Approaches
Combine multiple strategies:
- Content + collaborative
- Rules + machine learning
- Real-time + batch processing
- Personalized + popular

### AI-Powered Recommendations
Leverage AI models for intelligent recommendations:
- Embedding-based similarity
- Natural language understanding
- Multi-modal analysis
- Contextual reasoning

## Configuration

### Provider Setup
Configure recommendation providers in MemberJunction:
```typescript
{
    name: 'ContentBasedProvider',
    type: 'content',
    config: {
        similarityThreshold: 0.7,
        maxCandidates: 100,
        boostRecent: true
    }
}
```

### Scoring and Ranking
Control how recommendations are scored:
- Relevance scoring
- Diversity penalties
- Freshness boosts
- Business rule adjustments

### Filtering Rules
Apply constraints to recommendations:
- Category filters
- Age restrictions
- Availability checks
- Custom business logic

## Use Cases

### Content Discovery
Help users find relevant content:
```typescript
const articles = await engine.GetRecommendations({
    userId: currentUser.id,
    itemType: 'Articles',
    count: 5,
    context: {
        excludeRead: true,
        preferredCategories: user.interests
    }
});
```

### Product Recommendations
Suggest products based on behavior:
```typescript
const products = await engine.GetRecommendations({
    userId: customer.id,
    itemType: 'Products',
    count: 10,
    context: {
        cartItems: currentCart,
        priceRange: { min: 0, max: 100 }
    }
});
```

### Similar Items
Find items similar to current selection:
```typescript
const similar = await engine.GetSimilarItems({
    itemId: currentProduct.id,
    itemType: 'Products',
    count: 8,
    strategy: 'content-based'
});
```

## Performance Optimization

1. **Caching** - Cache recommendations for common requests
2. **Batch Processing** - Pre-compute recommendations offline
3. **Incremental Updates** - Update only changed items
4. **Feature Engineering** - Optimize feature extraction
5. **Index Optimization** - Use appropriate data structures

## Analytics and Monitoring

Track recommendation performance:
- Click-through rates
- Conversion metrics
- Coverage statistics
- Diversity measures
- User satisfaction

## Best Practices

1. **Start Simple** - Begin with basic algorithms and iterate
2. **Measure Impact** - Track recommendation effectiveness
3. **Handle Edge Cases** - New users, new items, sparse data
4. **Ensure Diversity** - Avoid filter bubbles
5. **Respect Privacy** - Handle user data appropriately

## Future Enhancements

Planned improvements:
- Real-time recommendation updates
- Multi-objective optimization
- Explainable recommendations
- Advanced personalization
- Cross-domain recommendations

## Contributing

When contributing to the recommendations framework:
1. Follow the provider pattern
2. Include performance metrics
3. Document algorithms used
4. Add comprehensive tests
5. Consider scalability

## License

The recommendations framework follows the same licensing as the MemberJunction project.