README.md

metadata

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:

{
  "items": ["item_id_1", "item_id_2"]
}

Response:

{
  "model": "similarity",
  "predictions": [
    {
      "item_ids": ["..."],
      "titles": ["..."],
      "scores": [0.95, 0.87, ...],
      "posters": ["https://...", ...],
      "premiere_years": [2023, 2022, ...]
    }
  ]
}

Example Usage

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.