# Recommendation Engine

A hybrid recommendation engine combining collaborative filtering with content-based scoring, served at sub-50ms latency.

## Key facts

- **Domain:** RETAIL
- **Status:** Delivered across e-commerce engagements
- **Use when:** E-commerce or omnichannel retailers want basket-size growth from contextual recommendations.
- **Reference engagement:** Available for new engagements

## The problem

Static cross-sell logic fails to adapt. Static recommendations miss revenue. Recommendations slower than 200ms get ignored.

## The approach

Hybrid model combining collaborative filtering on aggregated anonymised signals with content-based scoring for cold-start coverage. Per-segment weighting tunes recommendations to how each cohort actually shops.

## The architecture

Serving on Elasticsearch for sub-50ms latency across millions of items. Online evaluation harness exposes A/B configurations to the merchandising team.

## The outcome

Basket value grows. Cold-start items surface within their first sessions. The merchandising team can run experiments without an engineering ticket.

## ATI shape

- Predictive intelligence — Collaborative filtering + content-based scoring
- Agentic execution — A/B harness as a per-experiment workflow
- Secure data — Aggregated, anonymised signals only
- Actionable outcomes — Sub-50ms recommendations served per session

## Tech stack

- Python
- Elasticsearch
- Hybrid CF + content
- A/B harness

## Impact

- <50ms — Latency at the recommendation surface
- Hybrid — Collaborative + content for cold-start coverage
- A/B — Merchandising-controlled experiments

## Related

- [All accelerators](https://levent.ai/accelerators/)
- [Next: Customer Lifetime Value](https://levent.ai/accelerators/clv/)

Metrics on this page are estimated and expected improvements describing the design intent of the accelerator. Real-client delivered metrics stay in private decks; see https://levent.ai/ai-content-policy/ for the abstraction policy.

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**Canonical URL:** https://levent.ai/accelerators/recommendation-engine/