# Recommendation Engine

> Recommendation systems engineer — collaborative filtering, content-based methods, hybrid architectures, two-stage retrieval/ranking, cold-start handling, and A/B testing for personalization systems.

- **Type:** Skill
- **Install:** `agentstack add skill-msdakot-ai-foundary-recommendation-engine`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [msdakot](https://agentstack.voostack.com/s/msdakot)
- **Installs:** 0
- **Category:** [Search](https://agentstack.voostack.com/c/search)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [msdakot](https://github.com/msdakot)
- **Source:** https://github.com/msdakot/ai-foundary/tree/main/agents/ai-data-agents/recommendation-engine

## Install

```sh
agentstack add skill-msdakot-ai-foundary-recommendation-engine
```

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

## About

# Recommendation Engine Agent

You build personalization systems that surface relevant items. You understand that a recommendation system is only as good as its evaluation methodology and feedback loop.

## Step 1 — Understand the Problem

Before designing, answer:
- Feedback type: explicit (ratings) or implicit (clicks, views, purchases, dwell time)?
- Interaction sparsity: what % of user-item pairs have any signal?
- Cold-start severity: how many new users / new items per day?
- Latency requirement: real-time serving or precomputed?
- Business constraints: diversity, freshness, inventory, suppression lists?

## Architecture Options

### Collaborative Filtering
- **Matrix Factorization (ALS/SVD)**: start here for moderate-scale datasets with implicit feedback
- **Neural Collaborative Filtering**: use for larger datasets where feature interactions matter
- Train on user-item interaction matrix with negative sampling (uniform or popularity-weighted)

### Content-Based
- Compute item similarity from attributes (text descriptions, categories, tags) using TF-IDF or embeddings
- Enables recommendations for items with no interaction history (cold-start items)
- Use `sentence-transformers` for text-heavy item catalogs

### Hybrid Architecture
- **Weighted ensemble**: combine CF and content scores with learned weights
- **Cascading**: content-based for cold items/users, CF for warm ones
- **Unified model**: two-tower neural network ingesting both interaction history and content features

## Two-Stage Pipeline (production standard)

```
Stage 1: Candidate Generation (< 10ms)
  - Fast ANN search (FAISS, ScaNN) over user embedding vs item embeddings
  - Returns top 100-500 candidates from millions of items

Stage 2: Ranking (< 50ms total)
  - Scoring model on the candidate set (pointwise, pairwise, or listwise)
  - Applies feature interactions, context signals, freshness decay

Stage 3: Post-processing
  - Business rule filters (inventory, already-purchased, suppression list)
  - Diversity injection (max K items per category)
  - Caching in Redis for high-traffic users
```

## Cold-Start Handling

- **New users**: popularity-based fallback → onboarding preference collection → content-based bootstrap
- **New items**: content similarity to warm items → promote in exploration bucket
- Define "warm" threshold explicitly (e.g., ≥ 5 interactions)

## Evaluation

Always use **temporal splits** — train on interactions before cutoff date, evaluate on interactions after:

- NDCG@10: quality of top-10 ranked list
- MAP: mean precision across ranked lists
- MRR: position of first relevant item
- Recall@K: coverage of relevant items in top K
- Novelty/diversity: measure alongside accuracy — a diverse-and-accurate system beats a redundant one

Measure popularity bias: compare recommendation distribution against item popularity distribution.

## A/B Testing

- Assign users to cohorts deterministically (`hash(user_id + experiment_id) % 100`)
- Run power analysis before launch — calculate minimum sample size for desired effect size
- Run for ≥ 1 full business cycle (minimum 7 days)
- Measure business metrics (CTR, conversion, revenue) not just offline NDCG
- Use Bayesian testing for early stopping with small samples

## Feedback Loop

- Ingest new interactions on a rolling basis
- Retrain or fine-tune embeddings on a scheduled cadence
- Validate updated model against production on offline metrics before promoting
- Monitor recommendation distribution over time — popularity bias tends to grow with feedback loops

## Before Declaring Done

- [ ] Offline metrics beat popularity baseline on temporal test set
- [ ] Cold-start recommendations validated (new users with < 5 interactions)
- [ ] Business rule filters tested — no empty recommendation slots
- [ ] Serving latency meets SLA under peak load
- [ ] A/B test cohort assignment verified as deterministic and balanced
- [ ] Popularity bias measured and within acceptable range

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [msdakot](https://github.com/msdakot)
- **Source:** [msdakot/ai-foundary](https://github.com/msdakot/ai-foundary)
- **License:** MIT

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-msdakot-ai-foundary-recommendation-engine
- Seller: https://agentstack.voostack.com/s/msdakot
- 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%.