pocket-recs 0.1.0
CPU-only hybrid recommender (co-vis + ANN + LightGBM) with tiny footprint.
Requires Python: >=3.9
Keywords: ANN, FAISS, LightGBM, hnsw, ranking, recommendation

# Pocket-Recs

**A CPU-only hybrid recommender system combining research-proven patterns with minimal infrastructure requirements.**

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## Overview

**Tired of expensive recommendation services? Want to run recommendations on a $10/month VPS?**

Pocket-Recs is a production-ready recommendation engine that brings enterprise-grade ML to small and medium businesses. It scales from tiny shops to mid-sized catalogs (up to 100k items) while running on modest hardware (2 vCPU / 4-8 GB RAM). 

**No GPU needed. No cloud ML services. Just smart recommendations on a CPU.**

It combines:

- **Two-tower retrieval** with ANN (FAISS/HNSW) for semantic search
- **Co-visitation** patterns with time decay
- **Brand-popularity** with recency weighting
- **LightGBM LambdaRank** for re-ranking
- **MMR diversification** to reduce redundancy

All components are **CPU-optimized** and use **permissive open-source licenses** (Apache-2.0, MIT).

## When to Use Pocket-Recs

**✅ Perfect For:**
- E-commerce shops with 100-100k products
- Content platforms (blogs, videos, music) with small-to-medium catalogs
- Startups wanting production-quality recommendations without ML infrastructure
- Cost-conscious teams ($5-25/month hosting)
- CPU-only environments (edge, serverless, modest VPS)

**❌ Not Ideal For:**
- Mega-scale catalogs (>1M items) - consider specialized vector databases
- Real-time personalization with <1ms latency requirements
- Deep learning research requiring custom architectures
- Cases where GPU acceleration is already available and preferred

**🤔 Also Consider:**
- If you have <100 items: Simple collaborative filtering may be enough
- If you have >1M items: Look into DiskANN, Milvus, or Pinecone
- If you need real-time learning: Consider online learning frameworks

## Key Features

- **Hybrid Multi-Stage Architecture**: Retrieval → Ranking → Diversification
- **CPU-Only**: No GPU required for training or inference
- **Cost-Efficient**: Runs on a $5-10/month VPS
- **Fast**: 10-40ms p95 latency for 100k item catalogs
- **Explainable**: Returns reason codes for each recommendation
- **Configurable**: Extensive configuration options via Python API
- **Production-Ready**: REST API, health checks, artifact versioning

## Getting Started in 5 Minutes

```bash
# 1. Install
pip install pocket-recs[ann,api]

# 2. Generate sample data (or use your own)
python -c "from pocket_recs import Recommender; print('Ready!')"

# 3. Train the model (using example data from examples/ directory)
pocket-recs fit interactions.parquet catalog.csv artifacts/

# 4. Start the API server
pocket-recs serve artifacts/ catalog.csv --port 8000

# 5. Get recommendations
curl -X POST "http://localhost:8000/v1/recommend" \
  -H "Content-Type: application/json" \
  -d '{"user_id": "user_123", "k": 10}'
```

## Installation

```bash
# Basic installation (includes core dependencies)
pip install pocket-recs

# Recommended: Install with ANN support (FAISS/HNSW)
pip install pocket-recs[ann]

# Install with API server (FastAPI + uvicorn)
pip install pocket-recs[api]

# Install with ONNX optimization
pip install pocket-recs[onnx]

# Development installation (includes testing tools)
pip install pocket-recs[dev]

# All features (recommended for production)
pip install pocket-recs[ann,api,onnx]
```

**System Requirements:**
- Python 3.9 or higher
- 2-8 GB RAM (depending on catalog size)
- No GPU required (CPU-only)

## Quick Start

### 1. Prepare Your Data

You need two files: a **catalog** (CSV) with your items and **interactions** (Parquet) with user behavior.

**Catalog CSV** (`catalog.csv`):
```csv
item_id,brand,category,title,short_desc,price,in_stock
item_1,BrandA,Electronics,Wireless Headphones,Premium noise-canceling,199.99,true
item_2,BrandB,Electronics,USB-C Cable,Fast charging cable,12.99,true
item_3,BrandA,Electronics,Bluetooth Speaker,Portable wireless speaker,79.99,true
```

**Important**: 
- `item_id` and `title` are required
- Other fields are optional but improve recommendations
- Include all items you want to recommend (even if no interactions yet)

**Interactions Parquet** (`interactions.parquet`):
```python
import polars as pl

interactions = pl.DataFrame({
    "user_id": ["u1", "u1", "u2", "u2"],
    "item_id": ["item_1", "item_2", "item_1", "item_3"],
    "brand": ["BrandA", "BrandB", "BrandA", "BrandA"],
    "timestamp": [1700000000000, 1700000100000, 1700000200000, 1700000300000],
    "quantity": [1, 1, 2, 1],
    "price": [199.99, 12.99, 199.99, 79.99],
    "event": ["view", "add", "purchase", "view"]
})
interactions.write_parquet("interactions.parquet")
```

**Important**:
- `user_id`, `item_id`, `timestamp`, and `event` are required
- Use **milliseconds** for timestamps (not seconds!)
- Valid events: "view", "add", "purchase"
- More data = better recommendations (aim for 1000+ interactions minimum)

### 2. Train the Model

```python
from pocket_recs import fit

# Run offline training pipeline
artifacts_dir = fit(
    interactions_path="interactions.parquet",
    catalog_path="catalog.csv",
    out_dir="artifacts/"
)
```

Or via CLI:
```bash
pocket-recs fit interactions.parquet catalog.csv artifacts/
```

### 3. Generate Recommendations

```python
from pocket_recs import Recommender
from pocket_recs.types import RecommendRequest, Interaction

# Load recommender
rec = Recommender(
    artifacts_dir="artifacts/",
    catalog_path="catalog.csv"
)

# Get recommendations
request = RecommendRequest(
    user_id="u1",
    k=10,
    recent=[
        Interaction(
            user_id="u1",
            item_id="item_1",
            timestamp=1700000000000,
            event="view"
        )
    ]
)

response = rec.recommend(request)

for item in response.items:
    print(f"{item.rank}. {item.item_id} (score: {item.score:.3f})")
    print(f"   Reasons: {', '.join(item.reasons)}")
```

### 4. Serve via API

```bash
# Start FastAPI server
pocket-recs serve artifacts/ catalog.csv --port 8000

# Or programmatically
from pocket_recs.api import create_app
import uvicorn

app = create_app("artifacts/", "catalog.csv")
uvicorn.run(app, host="0.0.0.0", port=8000)
```

**API Endpoints:**

```bash
# Get recommendations
curl -X POST "http://localhost:8000/v1/recommend" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "u1",
    "k": 10,
    "recent": [
      {"user_id": "u1", "item_id": "item_1", "timestamp": 1700000000000, "event": "view"}
    ],
    "brand": "BrandA",
    "exclusions": ["item_1"]
  }'

# Health check
curl http://localhost:8000/healthz

# Readiness check
curl http://localhost:8000/readyz
```

**Available Request Parameters:**
- `user_id` (required): User identifier
- `k` (optional, default=20): Number of recommendations (1-100)
- `recent` (optional): List of recent user interactions
- `brand` (optional): Filter recommendations by brand
- `exclusions` (optional): List of item IDs to exclude from results
- `filters` (optional): Additional custom filters

Visit `http://localhost:8000/docs` for interactive API documentation.

## Architecture

### How It Works

Pocket-Recs uses a **multi-stage pipeline** to generate recommendations:

1. **Retrieval Stage**: Quickly fetch 100-200 candidate items from multiple sources
   - **Co-visitation**: "Users who viewed X also viewed Y" patterns
   - **Brand Popularity**: Trending items in preferred brands
   - **ANN Semantic Search**: Items similar to user's recent interests (via embeddings)

2. **Ranking Stage**: Re-score candidates using LightGBM LambdaRank model
   - Combines signals: relevance, recency, popularity, diversity
   - Learns from interaction patterns (views → adds → purchases)

3. **Diversification Stage**: Apply MMR to reduce redundancy
   - Ensures results aren't all from the same category/brand
   - Balances relevance with variety

4. **Explanation**: Attach reason codes to each recommendation
   - "co-visit", "brand-popular", "semantic-match", etc.
   - Helps with debugging and transparency

### Architecture Diagram

```
+--------------------+     +-----------------+      +-------------------+
|  Interactions      |---->|  Sessionize     |----->|  Co-visitation    |
+--------------------+     +-----------------+      +-------------------+
          |                                                  |
          v                                                  v
+--------------------+     +-------------------------+    +------------------+
| Brand Popularity   |     | Text Embeddings         |--->| HNSW/FAISS Index |
+--------------------+     +-------------------------+    +------------------+
          \                 /                                    |
           \               /                                     |
            v             v                                      v
           +-----------------+      +--------------------+
           | Candidate Pool  |      | User Vector        |
           +-----------------+      +--------------------+
                     |                             
                     v
              [Retrieval: ANN + Co-vis + Brand-pop]
                     |
                     v
              [Re-ranking: LightGBM LambdaRank]
                     |
                     v
              [Diversification: MMR]
                     |
                     v
            Top-N Recommendations + Reasons
```

## Performance

| Catalog Size | Index Size | Training Time | Inference (p95) | RAM Usage | Cost/Month |
|--------------|------------|---------------|-----------------|-----------|------------|
| 10k items    | ~15 MB     | 5-10 min      | 8-15 ms         | 2 GB      | $5-10      |
| 50k items    | ~75 MB     | 15-30 min     | 15-25 ms        | 4 GB      | $10-15     |
| 100k items   | ~150 MB    | 30-60 min     | 20-40 ms        | 6 GB      | $15-25     |

*Benchmarks on 2 vCPU / 4-8 GB RAM VPS with sentence-transformers/all-MiniLM-L6-v2*

## Configuration

All components are highly configurable. Here's a comprehensive example:

```python
from pocket_recs.config import (
    RecommenderConfig,
    EmbeddingConfig,
    ANNConfig,
    CovisConfig,
    BrandPopConfig,
    RankerConfig,
    MMRConfig,
)

config = RecommenderConfig(
    # Text embedding configuration
    embedding=EmbeddingConfig(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        batch_size=256,
        normalize=True,
        device="cpu",  # Use "cuda" for GPU
    ),
    
    # ANN index configuration
    ann=ANNConfig(
        M=16,                  # HNSW connectivity
        ef_construction=200,   # Build-time search depth
        ef_search=64,          # Query-time search depth
        metric="ip",           # Inner product similarity
    ),
    
    # Co-visitation configuration
    covis=CovisConfig(
        top_k=50,                    # Top items per seed
        tau_ms=15 * 60_000,          # Time decay: 15 minutes
        session_gap_minutes=30,      # Session boundary
    ),
    
    # Brand popularity configuration
    brand_pop=BrandPopConfig(
        half_life_days=7,      # Recency decay half-life
        top_n=100,             # Top items per brand
    ),
    
    # LightGBM ranker configuration
    ranker=RankerConfig(
        objective="lambdarank",
        metric="ndcg",
        ndcg_eval_at=[10],
        num_leaves=63,
        learning_rate=0.05,
        min_data_in_leaf=20,
        num_boost_round=500,
    ),
    
    # MMR diversification configuration
    mmr=MMRConfig(
        lambda_param=0.7,      # Relevance vs diversity (0=diverse, 1=relevant)
    ),
    
    # Candidate pool sizes
    candidate_sizes={
        "covis": 40,
        "brand_pop": 40,
        "ann": 80,
        "item2vec": 40,
    },
)

# Use custom config in training
artifacts_dir = fit(
    interactions_path="interactions.parquet",
    catalog_path="catalog.csv",
    out_dir="artifacts/",
    config=config,
)

# Use custom config in inference
recommender = Recommender(
    artifacts_dir="artifacts/",
    catalog_path="catalog.csv",
    config=config,
)
```

### Configuration Options Explained

**EmbeddingConfig**: Controls text-to-vector conversion
- `model_name`: Hugging Face model for embeddings
- `batch_size`: Batch size for encoding (adjust based on RAM)
- `normalize`: L2-normalize embeddings for cosine similarity
- `device`: "cpu" or "cuda" (training only, inference is CPU)

**ANNConfig**: Controls approximate nearest neighbor search
- `M`: HNSW graph connectivity (higher = better recall, more memory)
- `ef_construction`: Build-time search depth (higher = better index quality)
- `ef_search`: Query-time search depth (higher = better recall, slower)
- `metric`: "ip" (inner product) or "l2" (Euclidean distance)

**CovisConfig**: Controls co-visitation patterns
- `top_k`: Number of related items to keep per seed item
- `tau_ms`: Time decay parameter (items viewed together within this window are related)
- `session_gap_minutes`: Gap to split sessions (e.g., 30 minutes of inactivity)

**BrandPopConfig**: Controls brand-level trending items
- `half_life_days`: How quickly popularity decays (7 days = last week matters most)
- `top_n`: Keep top N items per brand

**RankerConfig**: Controls LightGBM learning-to-rank model
- `objective`: "lambdarank" for ranking optimization
- `num_leaves`: Tree complexity (higher = more expressive, risk overfitting)
- `learning_rate`: Gradient descent step size
- `num_boost_round`: Number of trees

**MMRConfig**: Controls result diversification
- `lambda_param`: 0.0 = maximize diversity, 1.0 = maximize relevance

**candidate_sizes**: How many candidates to retrieve from each source before ranking

## CLI Reference

```bash
# Train model
pocket-recs fit <interactions.parquet> <catalog.csv> <output_dir> [--model MODEL_NAME]

# Generate recommendations
pocket-recs recommend <artifacts_dir> <catalog.csv> [--user USER_ID] [--brand BRAND] [--top-k K]

# Start API server
pocket-recs serve <artifacts_dir> <catalog.csv> [--host HOST] [--port PORT] [--reload]

# Show version
pocket-recs version
```

## Data Schema

### Catalog (CSV)

| Column      | Type    | Required | Description                    |
|-------------|---------|----------|--------------------------------|
| item_id     | string  | Yes      | Unique item identifier         |
| title       | string  | Yes      | Product title                  |
| brand       | string  | No       | Brand name                     |
| category    | string  | No       | Product category               |
| short_desc  | string  | No       | Short description              |
| price       | float   | No       | Product price                  |
| in_stock    | boolean | No       | Stock availability             |

### Interactions (Parquet)

| Column     | Type   | Required | Description                        |
|------------|--------|----------|------------------------------------|
| user_id    | string | Yes      | User identifier                    |
| item_id    | string | Yes      | Item identifier                    |
| timestamp  | int64  | Yes      | Unix timestamp (milliseconds)      |
| event      | string | Yes      | Event type: view/add/purchase      |
| brand      | string | No       | Brand at interaction time          |
| quantity   | int    | No       | Quantity (default: 1)              |
| price      | float  | No       | Price at interaction time          |

## Research & References

This project implements patterns from leading recommendation systems research:

- **Two-Tower Retrieval**: YouTube DNN architecture (Covington et al., 2016)
- **Self-Attention Sequences**: SASRec (Kang & McAuley, 2018), BERT4Rec (Sun et al., 2019)
- **ANN Search**: HNSW (Malkov & Yashunin, 2016), FAISS (Johnson et al., 2017)
- **Learning-to-Rank**: LambdaRank/LambdaMART (Burges et al., 2006-2010)
- **MMR Diversification**: Maximal Marginal Relevance (Carbonell & Goldstein, 1998)
- **Collaborative Filtering**: Item2Vec (Barkan & Koenigstein, 2016), SPPMI (Levy & Goldberg, 2014)

See [REFERENCES.md](REFERENCES.md) for full citations and links.


## Roadmap

- [ ] Item2Vec/SPPMI collaborative embeddings
- [ ] Cross-encoder reranking (ONNX quantized)
- [ ] Sequential transformer features (SASRec/BERT4Rec)
- [ ] A/B testing utilities
- [ ] Offline evaluation metrics (NDCG@K, Recall@K, MAP@K)
- [ ] DiskANN support for >1M items
- [ ] OpenVINO INT8 quantization
- [ ] Streamlit demo UI
- [ ] Docker Compose deployment

## Troubleshooting

### Common Issues

**ImportError: No module named 'faiss' or 'hnswlib'**
```bash
pip install pocket-recs[ann]
```

**FastAPI/uvicorn not found**
```bash
pip install pocket-recs[api]
```

**Out of Memory during training**
- Reduce `batch_size` in `EmbeddingConfig` (try 128 or 64)
- Use a smaller embedding model (e.g., `sentence-transformers/paraphrase-MiniLM-L3-v2`)
- Process data in chunks if you have >1M interactions

**Slow recommendations**
- Reduce `ef_search` in `ANNConfig` (try 32 or 16)
- Reduce `candidate_sizes` for each retrieval source
- Ensure embeddings are memory-mapped for large catalogs

**Poor recommendation quality**
- Ensure you have enough interaction data (at least 1000+ interactions)
- Check that `timestamp` values are in milliseconds, not seconds
- Verify `event` types are one of: "view", "add", "purchase"
- Increase `candidate_sizes` to retrieve more candidates
- Adjust `lambda_param` in MMRConfig for more/less diversity

**FAISS not available on Windows/ARM**
```bash
# Use hnswlib-only installation
pip install pocket-recs hnswlib
```

## Frequently Asked Questions (FAQ)

### Do I need a GPU?
No! Pocket-Recs is designed to run entirely on CPU. Training and inference both work on modest hardware.

### What's the minimum amount of data needed?
- **Minimum**: 100+ items, 1000+ interactions, 50+ users
- **Recommended**: 1000+ items, 10k+ interactions, 500+ users
- More data = better recommendations!

### How often should I retrain the model?
- **Small catalogs (<10k items)**: Daily or weekly
- **Medium catalogs (10-50k)**: Weekly or bi-weekly
- **Large catalogs (50k+)**: Weekly or monthly
- Retrain when you notice quality degradation or add many new items

### Can I use this for cold-start items (new products)?
Yes! New items with no interactions will still appear in recommendations through:
- Semantic similarity (text embeddings from title/description)
- Brand popularity (if the brand has historical data)

### How do I handle seasonal or trending items?
- Use the `half_life_days` parameter in `BrandPopConfig` (default: 7 days)
- Lower values (3-5 days) give more weight to recent trends
- Higher values (14-30 days) smooth out short-term fluctuations

### Can I customize the ranking model?
Yes! You can:
1. Adjust `RankerConfig` parameters (num_leaves, learning_rate, etc.)
2. Add custom features to the ranking stage
3. Use a different ranker altogether (replace LightGBM with your own model)

### What's the difference between `recent` interactions and the training data?
- **Training data** (`interactions.parquet`): Historical data used to train models offline
- **Recent interactions** (in API request): Real-time user behavior used at inference time
- Recent interactions update the user's profile without retraining

### How do I deploy this to production?
See `examples/PRODUCTION_README.md` and `examples/PRODUCTION_API_GUIDE.md` for:
- Docker deployment
- Load balancing
- Monitoring and logging
- A/B testing
- Artifact versioning

### Can I use this with other languages (Java, Node.js, etc.)?
Yes! The REST API allows any language to consume recommendations. You can also:
- Call the Python API from other languages via process execution
- Export ONNX models for cross-platform inference (coming soon)

### How do I evaluate recommendation quality?
Use the testing scripts in `examples/`:
- `test_recommendations.py`: Basic quality checks
- `test_hybrid_recommendations.py`: Compare different strategies
- Implement offline metrics (NDCG@K, Recall@K) for your dataset

## Contributing

Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

## License

Apache License 2.0 - See [LICENSE](LICENSE) for details.

## Citation

If you use Pocket-Recs in your research or production systems, please cite:

```bibtex
@software{pocket_recs,
  title = {Pocket-Recs: CPU-Only Hybrid Recommender System},
  author = {Amjad},
  year = {2025},
  url = {https://github.com/amjad/pocket-recs}
}
```

## Acknowledgments

Built with:
- [sentence-transformers](https://www.sbert.net/) for text embeddings
- [FAISS](https://github.com/facebookresearch/faiss) / [hnswlib](https://github.com/nmslib/hnswlib) for ANN search
- [LightGBM](https://lightgbm.readthedocs.io/) for learning-to-rank
- [Polars](https://www.pola.rs/) for fast data processing
- [FastAPI](https://fastapi.tiangolo.com/) for REST API
- [Typer](https://typer.tiangolo.com/) for CLI

## Support

- **Issues**: [GitHub Issues](https://github.com/amjad/pocket-recs/issues)
- **Discussions**: [GitHub Discussions](https://github.com/amjad/pocket-recs/discussions)
- **Documentation**: [Read the Docs](https://pocket-recs.readthedocs.io/) (coming soon)

---

**Built with best practices, research-backed algorithms, and a focus on practical deployment.**