Recommendation System Designer

Designs recommendation systems covering collaborative filtering, content-based filtering, hybrid approaches, cold start solutions, embedding models, real-time serving, and A/B testing strategies for personalized user experiences.

Platinum

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

85.15

Research Quality (15%)

8.6

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.7

Completeness (10%)

8.4

User Satisfaction (20%)

8.4

Decision Usefulness (15%)

8.5

Best for

▸Design two-tower neural collaborative filtering models for e-commerce product recommendations
▸Build cold-start solutions for new users with content-based hybrid approaches
▸Implement real-time candidate generation and ranking pipelines for streaming platforms
▸Optimize recommendation diversity and mitigate filter bubbles in social media feeds
▸Design A/B testing frameworks for measuring recommendation system performance

What you'll get

●Multi-stage architecture diagram with ANN retrieval, neural ranking models, and specific technologies (FAISS, TensorFlow Serving)
●Feature engineering strategy combining implicit signals, content features, and contextual data with embedding dimensions
●Evaluation framework including offline metrics (NDCG, MAP) and online A/B testing methodology with business KPIs

Not designed for ↓

×General machine learning model training or hyperparameter tuning
×Building basic search engines or information retrieval systems
×Creating simple rule-based filtering systems
×Database design for storing user preferences

Expects

Clear description of the recommendation domain, user-item interaction data availability, catalog size, latency requirements, and specific business objectives.

Returns

Complete system architecture with candidate generation strategy, ranking models, cold-start handling, evaluation metrics, and production deployment considerations.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

recommendation-systemcollaborative-filteringcontent-basedcold-startembeddingtwo-towerpersonalizationab-testingrankingcandidate-generationmatrix-factorization

Research Foundation: 7 sources (5 paper, 1 official docs, 1 books)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

Feature Engineering StrategistGold

Works well with

A/B Test AnalystPlatinum Embedding Space ArchitectPlatinum ML Model Evaluation ExpertGold Real-Time Feature Serving ExpertGold Vector Database OptimizationGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Neural Network DebuggerPlatinum PyTorch Deep Learning EngineerPlatinum MLOps Platform EngineerGold

Common Workflows

Production ML Recommendation Pipeline

End-to-end workflow from feature design through recommendation system architecture to production deployment and monitoring

Feature Engineering Strategist→recommendation-system-designer→Real-Time Feature Serving Expert→MLOps Platform Engineer

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