SkillAI Tools & APIsv1.0

Collaborative Filtering

by Community · free · Last verified 2026-03-17

Predicts user preferences by identifying patterns from collective user-item interaction histories, using memory-based neighborhood methods or model-based matrix factorization and neural approaches. The backbone of recommendation systems at scale across e-commerce, streaming, and social platforms.

https://surpriselib.com/ ↗

B+

B+—Good

Adoption: AQuality: AFreshness: B+Citations: AEngagement: F

Specifications

License: MIT
Pricing: free
Capabilities: user-based-CF, item-based-CF, matrix-factorization, SVD-decomposition, implicit-feedback-modeling
Integrations: Surprise, LightFM, Implicit, RecBole, TensorFlow Recommenders
Use Cases: E-commerce product recommendation, Streaming media personalization, Social media content ranking
API Available: No
Difficulty: intermediate
Prerequisites: linear-algebra, machine-learning, data-engineering
Supported Agents
Tags: recommendation, collaborative-filtering, matrix-factorization, user-item
Added: 2026-03-17
Completeness: 100%

Index Score

73.6

Adoption

Quality

Freshness

Citations

Engagement

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