Agent skill · research science · aj-geddes
Recommendation System
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
Why this skill is useful
Provides implementation details and domain-specific techniques for building recommendation systems that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Recommendation System Overview This skill implements collaborative and content-based recommendation systems with matrix factorization techniques to predict user preferences, increase engagement, and drive conversions through personalized item suggestions. When to Use Developing recommendation features to improve user engagement and retention Implementing personalized product suggestions to increase sales and conversion rates Building hybrid recommendation systems that combine collaborative and content-based approaches Analyzing and optimizing recommendation coverage, diversity, and accuracy Handling sparse user-item interaction matrices and cold start scenarios Running A/B tests to measure the impact of recommendation algorithms on business metrics Approaches Collaborative Filtering: Users similar to you liked X Content-based: Items similar to what you liked Hybrid: Combining multiple approaches Matrix Factorization: Latent factor models Deep Learning: Neural networks for embeddings Key Metrics Precision@K: % recommendations relevant Recall@K: % relevant items found NDCG: Ranking quality metric Coverage: % items recommended Diversity: Variety in recommendations Implementation with Python Algorithm Comparison Collaborative Filtering: Simple, no content needed Content-based: Works with cold starts Matrix Factorization: Scalable, finds latent patterns Deep Learning: Complex patterns, requires data Hybrid: Combines strengths of multiple approaches Implementation Considerations Handling cold start (new users/items) Computational efficiency at scale Addressing sparsity (most items not rated) Diversity vs relevance trade-off Real-time vs batch recommendations Deliverables User-item interaction matrix Similarity matrices Recommendations for sample users Evaluation metrics (precision, recall, NDCG) Coverage and diversity analysis Visualization of results Production implementation code
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/recommendation-system