Can this skill help with large-scale datasets?

It provides implementation patterns designed for scalability, including matrix factorization and efficient data loading strategies for high-volume environments.

Does it include performance evaluation tools?

Yes, it provides code to calculate essential metrics including Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG) to measure accuracy.

What types of recommendation systems can I build?

You can build collaborative filtering, content-based filtering, and hybrid systems tailored to your specific dataset and user interaction patterns.

Which Python libraries are supported?

The skill generates implementation code using industry-standard libraries such as scikit-learn, TensorFlow, and PyTorch.

How does it handle the cold start problem?

The skill incorporates best practices like content-based fallbacks and popularity-based ranking to ensure recommendations work even for new users or items.

AI Recommendation Engine Builder

Name: AI Recommendation Engine Builder
Author: jeremylongshore

byjeremylongshore

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883

数据科学与机器学习

Constructs personalized recommendation systems using collaborative, content-based, and hybrid filtering techniques.

关于

This skill empowers Claude to design and implement sophisticated recommendation engines by analyzing user preferences and item features. It automates the end-to-end workflow—from data preprocessing and algorithm selection to model training and performance evaluation using metrics like NDCG. Whether you are building a movie suggestion engine, an e-commerce product recommender, or a personalized news feed, this skill provides the Python code and best practices necessary to handle scalability and the 'cold start' problem effectively.

主要功能

Implements collaborative and content-based filtering algorithms
Addresses cold start, scalability, and bias challenges
Provides performance evaluation with precision, recall, and NDCG metrics
Generates training code for scikit-learn, TensorFlow, and PyTorch
883 GitHub stars
Automates data preprocessing and feature extraction workflows

使用场景

Building a personalized movie or media suggestion engine
Implementing content ranking for news or blog websites
Creating product recommendations for e-commerce platforms

关于

主要功能

Implements collaborative and content-based filtering algorithms
Addresses cold start, scalability, and bias challenges
Provides performance evaluation with precision, recall, and NDCG metrics
Generates training code for scikit-learn, TensorFlow, and PyTorch
883 GitHub stars
Automates data preprocessing and feature extraction workflows

使用场景

Building a personalized movie or media suggestion engine
Implementing content ranking for news or blog websites
Creating product recommendations for e-commerce platforms