Recommendation System

If you're building personalized product suggestions or trying to boost engagement through recommendations, this handles both collaborative and content-based filtering with matrix factorization under the hood. It's designed for the practical stuff: dealing with sparse interaction data, cold start problems, and running A/B tests to see if your recommendations actually move metrics. The skill covers hybrid approaches when you need to combine multiple techniques. It's from aj-geddes' useful-ai-prompts collection, which has 245 GitHub stars and passes security audits. Good fit if you're implementing recommendation features and want guidance on handling the common edge cases that trip people up.