# 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'.

- **Type:** Skill
- **Install:** `agentstack add skill-asgard-ai-platform-skills-algo-rec-mf`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [asgard-ai-platform](https://agentstack.voostack.com/s/asgard-ai-platform)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [asgard-ai-platform](https://github.com/asgard-ai-platform)
- **Source:** https://github.com/asgard-ai-platform/skills/tree/main/algo-rec-mf
- **Website:** https://github.com/asgard-ai-platform

## Install

```sh
agentstack add skill-asgard-ai-platform-skills-algo-rec-mf
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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](https://github.com/asgard-ai-platform)
- **Source:** [asgard-ai-platform/skills](https://github.com/asgard-ai-platform/skills)
- **License:** MIT
- **Homepage:** https://github.com/asgard-ai-platform

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-asgard-ai-platform-skills-algo-rec-mf
- Seller: https://agentstack.voostack.com/s/asgard-ai-platform
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.