Algo Rec Mf
Implement matrix factorization to decompose user-item interaction matrices into latent factor representations. Use this skill when the user needs scalable collaborative filtering, latent feature discovery, or dimensionality reduction for recommendation — even if they say 'SVD recommendations', 'latent factors', or 'factorize the rating matrix'.
Install
$ agentstack add skill-asgard-ai-platform-skills-algo-rec-mf ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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Matrix Factorization
Overview
Matrix factorization decomposes the user-item interaction matrix R (m×n) into two low-rank matrices: U (m×k) and V (n×k), where k > U[2] correctly captures user 0's higher ratings.
Edge Cases
| Input | Expected | Why | |-------|----------|-----| | User with 1 rating | Poor predictions for that user | Insufficient data to learn user factors | | Highly popular item | Predicted near average | Dominant first latent factor captures popularity | | All ratings = 5 | Trivial factorization | No variance to learn from |
Gotchas
- Implicit data needs different loss: For clicks/views (no explicit ratings), use weighted matrix factorization (Hu et al. 2008) with confidence weighting, not RMSE.
- Cold start remains: New users/items have no entries in R. MF can't factorize what doesn't exist. Use side features or hybrid approaches.
- Negative sampling: For implicit feedback, you must sample negative examples (unobserved ≠ disliked). Random negative sampling works but biased sampling is better.
- Initialization matters: Random initialization can converge to poor local optima. SVD-based warm-start often helps.
- Bias terms: Add user bias bᵢ and item bias bⱼ: r̂ᵢⱼ = μ + bᵢ + bⱼ + uᵢ·vⱼ. This captures systematic rating tendencies.
References
- For ALS vs SGD comparison, see
references/optimization-comparison.md - For implicit feedback matrix factorization, see
references/implicit-mf.md
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: asgard-ai-platform
- Source: asgard-ai-platform/skills
- License: MIT
- Homepage: https://github.com/asgard-ai-platform
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.