Agent skill · research science · aj-geddes
Recommendation Engine
Build recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and neural network approaches
Why this skill is useful
Provides domain-specific knowledge and implementation patterns for building recommendation systems that the AI wouldn't reliably generate on its own.
What it needs
Requires matplotlib, numpy, pandas, scikit-learn, scipy installed locally. About 5k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Recommendation Engine Overview This skill provides comprehensive implementation of recommendation systems using collaborative filtering, content-based filtering, matrix factorization, and hybrid approaches to predict user preferences and deliver personalized suggestions. When to Use Building personalized product recommendations for e-commerce platforms Creating content recommendation systems for streaming services, news platforms, or social media Implementing user-user or item-item collaborative filtering based on interaction patterns Addressing cold start problems for new users or items with limited interaction history Evaluating recommendation quality using precision@k, recall@k, and NDCG metrics Scaling recommendation systems to handle millions of users and items efficiently Recommendation Approaches Collaborative Filtering: Using user-item interaction patterns Content-Based: Recommending similar items based on features Hybrid: Combining multiple approaches Matrix Factorization: Decomposing user-item matrix Neural Networks: Deep learning for embeddings Knowledge-Based: Using domain knowledge and rules Key Techniques User-User Similarity: Finding similar users Item-Item Similarity: Finding similar items Latent Factors: Hidden patterns in data Embeddings: Vector representations of users/items Graph-Based: Social networks and item graphs Python Implementation Algorithm Comparison User-CF: Good for diverse preferences, but suffers from sparsity Item-CF: Better for stable preferences, works well with sparse data SVD: Captures latent factors, computationally efficient Neural Networks: Captures complex patterns, requires more data Common Challenges Cold Start: New users/items with no interaction history Sparsity: Sparse interaction matrices Scalability: Handling millions of users and items Diversity: Avoiding recommendation bubbles Fairness: Representing all item types fairly Deliverables Recommendation model Ranked item predictions Evaluation metrics A/B testing framew …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/recommendation-engine