Can it handle both user-based and item-based collaborative filtering?

Yes, Claude can implement various collaborative filtering techniques, including user-user, item-item, and matrix factorization methods.

Can I use this for real-time recommendation updates?

Yes, while the skill focuses on building the engine, it provides scalable patterns that can be integrated into real-time pipelines for production environments.

Which machine learning libraries does this skill use?

The skill typically generates code using industry-standard Python libraries such as scikit-learn, TensorFlow, and PyTorch, depending on the complexity of the recommendation task.

How do I evaluate the quality of the recommendations generated?

The skill includes best practices for evaluation, providing implementation patterns for metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision@K, and Recall@K.

Does this skill help with data preparation?

Absolutely. It generates code for data loading, cleaning, normalization, and feature engineering to ensure your recommendation model performs accurately.

Recommendation Engine Builder

Name: Recommendation Engine Builder
Author: jeremylongshore

byjeremylongshore

•

883

データサイエンスとML

Builds personalized recommendation systems using collaborative filtering, content-based filtering, and hybrid machine learning approaches.

概要

The Recommendation Engine Builder skill empowers Claude to design and implement sophisticated personalization systems tailored to specific datasets and business requirements. It automates the selection of appropriate algorithms, data preprocessing, and model training using popular libraries like scikit-learn, TensorFlow, and PyTorch. Whether you are addressing the cold-start problem or scaling to large user bases, this skill provides production-ready implementation patterns for ranking items by relevance, making it an essential tool for developers building e-commerce platforms, content discovery engines, or personalized media services.

主な機能

Support for Collaborative, Content-based, and Hybrid filtering algorithms
Scalable implementation patterns for large-scale user-item interaction data
883 GitHub stars
Comprehensive model evaluation using metrics like NDCG, precision, and recall
Automated data preprocessing and feature extraction from item metadata
Advanced strategies for mitigating bias and solving the cold-start problem

ユースケース

Building a personalized product recommendation engine for e-commerce platforms
Developing movie or music suggestion systems based on user rating history
Creating content discovery algorithms for news websites and digital libraries

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills recommendation-engine

For use in Claude.ai and ChatGPT

Download Skill

GitHub

概要