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Recommendation Engine

Predict user preferences and generate personalized item recommendations from interaction data.

Updated May 17, 2026
Search Tags:#embeddings#recommendation#hybrid#content-based#matrix-factorization#collaborative-filtering
One-click install
npx skills add https://github.com/cenjie/skills --skill recommendation-engine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Recommendation Engine
Source: https://github.com/cenjie/skills/tree/main/skills/recommendation-engine
Command: npx skills add https://github.com/cenjie/skills --skill recommendation-engine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill enables building and deploying personalized recommendations by analyzing user interactions and item attributes to predict what users will like next.

Core Features & Use Cases

  • Collaborative Filtering: derive user and item similarities from interaction data to generate recommendations.
  • Content-Based & Hybrid Approaches: leverage item features and user profiles to improve relevance.
  • Evaluation & Deployment: use metrics like precision@k, recall@k, and NDCG to assess quality and scale to large datasets; supports end-to-end workflows from data preparation to model deployment.
  • Real-World Scenarios: ecommerce product recommendations, streaming content curation, or news feeds, with cold-start handling.

Quick Start

Train a basic model using your user-item data and generate top-5 recommendations for a target user.

Frequently Asked Questions about Recommendation Engine

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a hybrid recommendation system for user-item interactions?▼

Build a hybrid recommendation system by combining collaborative filtering and content-based filtering to leverage both user interaction data and item metadata, improving recommendation relevance and accuracy.

What is the best way to evaluate recommendation quality using precision@k and NDCG?▼

Evaluate recommendation quality using precision@k, recall@k, and NDCG metrics to measure how accurately the model ranks relevant items within the top predicted results.

How does collaborative filtering predict user preferences for personalized recommendations?▼

Collaborative filtering predicts user preferences by deriving user and item similarities from historical interaction data to generate personalized recommendations.

Can I use neural embeddings and matrix factorization for scalable recommendation deployment?▼

Use neural embeddings and matrix factorization to process large datasets and scale recommendation deployment from data preparation to end-to-end model serving.

When do I need content-based filtering for cold-start handling in news feeds?▼

Use content-based filtering for cold-start handling when lacking historical user interaction data, leveraging item features and user profiles to suggest relevant news or content.

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