What types of recommendation models can this skill build?

This skill supports collaborative filtering, content-based filtering, and hybrid approaches to provide highly personalized user experiences.

Which Python libraries are supported?

The skill typically generates code using industry-standard libraries including scikit-learn, TensorFlow, and PyTorch.

Can I use this for e-commerce websites?

Absolutely. It is ideal for building product recommendation engines that analyze purchase history and item attributes to boost conversion rates.

Does it provide code for model evaluation?

Yes, the skill generates code that includes performance metrics such as precision, recall, and NDCG to ensure your recommendations are accurate.

How does it handle the cold start problem?

The generated code incorporates best practices for recommendation systems, including strategies to manage new users or items with limited interaction data.

Recommendation System Builder

Name: Recommendation System Builder
Author: intent-solutions-io

byintent-solutions-io

数据科学与机器学习

Builds personalized recommendation engines using collaborative filtering, content-based filtering, and hybrid machine learning models.

关于

The Recommendation System Builder skill empowers Claude to architect and implement sophisticated recommendation engines by analyzing user preferences and item characteristics. Whether you need to build an e-commerce product suggestion tool or a media discovery platform, this skill automates the complex tasks of data preprocessing, algorithm selection, and performance evaluation. It provides production-ready Python code that incorporates industry best practices, addressing common challenges like scalability and the 'cold start' problem to deliver highly relevant, personalized content to users.

主要功能

0 GitHub stars
Automated algorithm selection for collaborative and content-based filtering
Python code generation using scikit-learn, TensorFlow, and PyTorch
Model evaluation using precision, recall, and NDCG metrics
Advanced data preprocessing and feature extraction pipelines
Implementation patterns for handling cold starts and scalability

使用场景

Creating personalized movie or media recommendation platforms
Developing content ranking systems for news and blog sites
Building e-commerce product suggestion engines based on user history

关于

主要功能

0 GitHub stars
Automated algorithm selection for collaborative and content-based filtering
Python code generation using scikit-learn, TensorFlow, and PyTorch
Model evaluation using precision, recall, and NDCG metrics
Advanced data preprocessing and feature extraction pipelines
Implementation patterns for handling cold starts and scalability

使用场景

Creating personalized movie or media recommendation platforms
Developing content ranking systems for news and blog sites
Building e-commerce product suggestion engines based on user history