recommendation-engine
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
npx skills add majiayu000/claude-skill-registry --skill recommendation-engine-secondsky-claude-skills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Recommendation Engine Build recommendation systems for personalized content and product suggestions. ## Recommendation Approaches | Approach | How It Works | Pros | Cons | |----------|--------------|------|------| | Collaborative | User-item interactions | Discovers hidden patterns | Cold start | | Content-based | Item features | Works for new items | Limited discovery | | Hybrid | Combines both | Best of both | Complex | ## Collaborative Filtering ```python import numpy as np from scipy.sparse import csr_matrix from sklearn.metrics.pairwise import cosine_similarity class CollaborativeFilter: def __init__(self): self.user_similarity = None self.item_similarity = None def fit(self, user_item_matrix): # User-based similarity self.user_similarity = cosine_similarity(user_item_matrix) # Item-based similarity self.item_similarity = cosine_similarity(user_item_matrix.T) def recommend_for_user(self, user_id, n=10): scores = self.user_similarity[user_id].dot(self.user_item_matrix) # Exclude already interacted items already_interacted = self.user_item_matrix[user_id].nonzero()[0] scores[already_interacted] = -np.inf return np.argsort(scores)[-n:][::-1] ``` ## Matrix Factorization (SVD) ``
- Recommendation Approaches
- Collaborative Filtering
- Matrix Factorization (SVD)
- Hybrid Recommender
- Evaluation Metrics
- Cold Start Solutions
- Quick Start: Build a Recommender in 5 Steps
- Known Issues Prevention
- 1. Popularity Bias
- 2. Data Sparsity (Matrix >99% Empty)
- 3. Cold Start Without Fallback
- 4. Not Excluding Already-Interacted Items
- 5. Ignoring Implicit Feedback Confidence
- 6. Not Evaluating Ranking Quality (Using Only Accuracy)
What does the recommendation-engine skill do?
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill recommendation-engine-secondsky-claude-skills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.