Agent skill · research science · jeremylongshore
building-recommendation-systems
Execute this skill empowers AI assistant to construct recommendation
Why this skill is useful
Generates executable Python code for building recommendation systems using various algorithms and best practices that aren't commonly known.
What it needs
Requires pytorch, scikit-learn, tensorflow installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Recommendation Engine Build recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches tailored to specific datasets and use cases. Overview design and implement recommendation systems tailored to specific datasets and use cases. It automates the process of selecting appropriate algorithms, preprocessing data, training models, and evaluating performance, ultimately providing users with a functional recommendation engine. How It Works 1. Analyzing Requirements: Claude identifies the type of recommendation needed (collaborative, content-based, hybrid), data availability, and performance goals. 2. Generating Code: Claude generates Python code using relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch) to build the recommendation model. This includes data loading, preprocessing, model training, and evaluation. 3. Implementing Best Practices: The code incorporates best practices for recommendation system development, such as handling cold starts, addressing scalability, and mitigating bias. When to Use This Skill This skill activates when you need to: Build a personalized movie recommendation system. Create a product recommendation engine for an e-commerce platform. Implement a content recommendation system for a news website. Examples Example 1: Personalized Movie Recommendations User request: "Build a movie recommendation system using collaborative filtering." The skill will: 1. Generate code to load and preprocess movie rating data. 2. Implement a collaborative filtering algorithm (e.g., matrix factorization) to predict user preferences. Example 2: E-commerce Product Recommendations User request: "Create a product recommendation engine for an online store, using content-based filtering." The skill will: 1. Generate code to extract features from product descriptions and user purchase history. 2. Implement a content-based filtering algorithm to recommend similar products. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/building-recommendation-systems--200980