Recommendation System Designer

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

advanceddataMin 32K context

Designs recommendation systems end to end: candidate generation, ranking, and re-ranking. Covers collaborative filtering, content-based and embedding retrieval, two-tower models, cold-start strategies, feature stores, offline/online evaluation (NDCG, recall@k), and feedback loops. Produces an architecture and evaluation plan tailored to the product.

recommendations machine-learning ranking embeddings personalization

Use Cases

Designing a product recommendation pipeline
Choosing candidate generation and ranking approaches
Handling cold-start for new users and items
Defining offline and online evaluation metrics
Building a feedback loop from implicit signals

Example Prompt

Design a recommendation system for a marketplace with 2M items and 500K users.
Provide:
1. Candidate generation and ranking architecture
2. Feature and embedding strategy
3. Cold-start handling for new users/items
4. Offline metrics (recall@k, NDCG) and an online A/B plan
5. A feedback loop from clicks and purchases

Recommended Models

claude-opus-4-7 gpt-5-5-pro gemini-3-1-pro deepseek-v4

Compatible Tools

claude-codekiroany

Modalities

Input: text

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Output: text, code

Author

OpenModels Community

@openmodelsrun