# WIA-AI-024 Recommendation AI - PHASE 1: Foundation

## Overview

PHASE 1 establishes the foundational components of a recommendation system, focusing on core algorithms, data structures, and basic evaluation metrics.

## Objectives

- Implement basic collaborative filtering algorithms
- Establish content-based filtering fundamentals
- Define standard data structures and interfaces
- Set up basic evaluation framework

## Technical Requirements

### 1.1 Data Structures

```typescript
interface UserInteraction {
    userId: string;
    itemId: string;
    interactionType: 'view' | 'click' | 'purchase' | 'rating';
    value?: number;
    timestamp: Date;
    context: InteractionContext;
}

interface ItemProfile {
    itemId: string;
    features: Record<string, any>;
    metadata: Record<string, any>;
    embeddings?: number[];
}

interface UserProfile {
    userId: string;
    preferences: number[];
    demographics?: Record<string, any>;
    history: UserInteraction[];
}
```

### 1.2 Collaborative Filtering

**User-Based CF:**
- Similarity metrics: Cosine, Pearson, Jaccard
- K-nearest neighbors (K=10-50)
- Weighted aggregation of ratings

**Item-Based CF:**
- Precompute item-item similarities
- Cache similarity matrix
- Efficient sparse matrix operations

### 1.3 Content-Based Filtering

**Feature Extraction:**
- TF-IDF for text content
- Structured feature vectorization
- Category encoding (one-hot, embeddings)

**Profile Building:**
- Weighted average of liked items
- Temporal decay of old preferences
- Negative feedback incorporation

### 1.4 Evaluation Metrics

**Accuracy:**
- RMSE (Root Mean Square Error)
- MAE (Mean Absolute Error)
- Precision@K, Recall@K

**Ranking:**
- NDCG@K
- Mean Average Precision (MAP)
- Mean Reciprocal Rank (MRR)

## Implementation Guidelines

### Sparse Matrix Handling

```typescript
class SparseMatrix {
    private data: Map<string, Map<string, number>>;

    set(row: string, col: string, value: number): void;
    get(row: string, col: string): number | undefined;
    getRow(row: string): Map<string, number>;
    getSparsity(): number;
}
```

### Similarity Computation

```typescript
interface SimilarityMetric {
    calculate(a: number[], b: number[]): number;
}

class CosineSimilarity implements SimilarityMetric {
    calculate(a: number[], b: number[]): number {
        // Cosine similarity implementation
    }
}
```

## Performance Requirements

- **Latency:** < 500ms for recommendations
- **Throughput:** > 100 requests/second
- **Accuracy:** RMSE < 1.0 for rating predictions
- **Coverage:** > 80% of catalog

## Testing Requirements

- Unit tests for all algorithms
- Integration tests for data pipeline
- Performance benchmarks
- Accuracy validation on standard datasets

## Deliverables

1. Core algorithm implementations
2. Data structure definitions
3. Evaluation framework
4. Unit and integration tests
5. Performance benchmarks
6. Documentation

## Success Criteria

- All unit tests passing
- Performance requirements met
- Accuracy baselines established
- Documentation complete

---

**Status:** Foundation ✅
**Next Phase:** [PHASE-2.md](PHASE-2.md) - Advanced Algorithms

弘益人間 (홍익인간) · Benefit All Humanity