Does it handle data preprocessing for ML?

Yes, the skill generates code to clean, format, and extract features from user-item interaction data and item descriptions automatically.

Which Python libraries are used in the generated code?

The skill typically utilizes standard data science libraries such as scikit-learn, TensorFlow, PyTorch, and Pandas to ensure production-ready code.

How is the performance of the recommendation engine measured?

The generated code includes evaluation logic using standard metrics like precision, recall, and NDCG (Normalized Discounted Cumulative Gain) to ensure accuracy.

What types of recommendation models can this skill build?

It supports collaborative filtering (such as matrix factorization), content-based filtering based on item metadata, and hybrid approaches that combine multiple methodologies.

Can this skill help with the cold start problem?

Yes, it incorporates best practices for recommendation systems, including strategies to handle new users or items where historical interaction data is missing.

Recommendation System Engine

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

byintent-solutions-io

データサイエンスとML

Automates the design and implementation of personalized recommendation systems using collaborative, content-based, and hybrid filtering techniques.

概要

This skill empowers Claude to architect sophisticated recommendation engines by analyzing user preferences, item features, and interaction datasets. It streamlines the entire machine learning workflow—from selecting optimal algorithms and preprocessing raw data to training models and evaluating performance with industry-standard metrics. By incorporating best practices for handling cold starts and ensuring scalability, this skill enables developers to quickly build and deploy functional recommenders for e-commerce, media streaming, and content discovery applications.

主な機能

Automates data preprocessing and feature extraction workflows
Supports collaborative, content-based, and hybrid filtering algorithms
Generates performance evaluation code using NDCG, precision, and recall metrics
0 GitHub stars
Integrates with major ML libraries including scikit-learn and TensorFlow
Addresses cold start problems and scalability during model design

ユースケース

Building personalized movie or media streaming recommendation engines
Developing product suggestion systems for e-commerce platforms
Creating content discovery and ranking systems for news or blog sites

概要

主な機能

Automates data preprocessing and feature extraction workflows
Supports collaborative, content-based, and hybrid filtering algorithms
Generates performance evaluation code using NDCG, precision, and recall metrics
0 GitHub stars
Integrates with major ML libraries including scikit-learn and TensorFlow
Addresses cold start problems and scalability during model design

ユースケース

Building personalized movie or media streaming recommendation engines
Developing product suggestion systems for e-commerce platforms
Creating content discovery and ranking systems for news or blog sites