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title: FF1000 Recommendation Service
emoji: 🎬
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
---
# FF1000 - ML Recommendation Service
A pretrained recommendation service for content discovery, providing similarity-based and personalized recommendations.
## API Endpoints
### Health Check
```
GET /health
```
Returns `{"status": "ok"}` when the service is running.
### Predict Endpoint
```
POST /predict/<model_name>
Content-Type: application/json
```
**Available models:**
- `similarity` - Find similar content based on embeddings
- `rfy` - "Recommended for you" personalized recommendations
- `nfm` - "Not for me" content filtering
**Request body:**
```json
{
"items": ["item_id_1", "item_id_2"]
}
```
**Response:**
```json
{
"model": "similarity",
"predictions": [
{
"item_ids": ["..."],
"titles": ["..."],
"scores": [0.95, 0.87, ...],
"posters": ["https://...", ...],
"premiere_years": [2023, 2022, ...]
}
]
}
```
## Example Usage
```bash
curl -X POST https://YOUR-SPACE.hf.space/predict/similarity \
-H "Content-Type: application/json" \
-d '{"items": ["ab553cdc-e15d-4597-b65f-bec9201fd2dd"]}'
```
## Architecture
The service loads pre-computed embeddings and serves three recommendation models:
- **Similarity**: Cosine distance between content embeddings
- **RFY**: Variance-explained recommendations for personalization
- **NFM**: Negative preference modeling
Built with Flask and scikit-learn.
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