Recommendation EngineDevelopment
Suggest the next product, article or course to each visitor from what people like them actually did, and prove it helps with a controlled test.
Recommendation Engine Development: what the work involves
Hand-picked best-seller carousels treat every visitor the same. A returning customer who bought a printer sees the same homepage as a first-time visitor browsing laptops. Catalogues with thousands of items bury good products, and merchandisers cannot manually curate related-item lists for each one, so cross-sell opportunities go unused.
We begin with the data you already hold: orders, views, carts, ratings, and the item catalogue. For most small and mid-sized shops a simple approach wins first: item-to-item co-purchase and co-view scores, blended with content similarity from titles, categories and embeddings so new items are not ignored. Where history is rich we add matrix factorisation or a gradient-boosted ranker. Business rules sit on top, such as stock availability, margin floors and excluded categories. The engine is served through a fast API and shown in carousels, emails or app screens. We validate offline on held-out purchases, then run an A/B test before widening the rollout.
Core features
Co-purchase and co-view logic
Items bought or viewed together by many customers are linked, forming the backbone of related-item suggestions.
Content-based cold start
New products with no history are matched by their attributes and text, so they are not invisible.
Personal homepage ranking
Returning visitors see items ordered by what their own history and similar customers suggest.
Business rule layer
Out-of-stock, low-margin or restricted items are filtered or boosted according to your commercial priorities.
Email and app surfaces
The same engine feeds website carousels, cart add-ons, post-purchase emails and mobile screens through one API.
A/B test measurement
Recommendations are tested against the existing experience on real traffic, with results reported plainly.
What we get right before launch
Too little data
A store with a few hundred orders cannot support heavy personalisation. We say so, begin with simple related-item logic, and add sophistication as the data grows.
Popularity feedback loops
Showing best-sellers makes them sell more and crowds out everything else. We include diversity and exploration so the long tail still gets seen.
Privacy of behaviour data
Browsing history is personal. We use first-party data, respect consent choices, avoid sensitive inferences, and let customers see or reset what drives suggestions.
Tools and technology
- Python
- scikit-learn
- XGBoost
- pgvector
- FastAPI
- PostgreSQL
- Redis
- Next.js
Common questions, answered
How much data do we need?
Simple related-item recommendations can work with a few thousand orders or sessions. Rich personalisation needs more. We examine your data first and recommend the lightest approach that is realistically supported.
Does it work for a new store?
Partly. Without history, suggestions rely on product attributes and manual rules. As orders accumulate, behaviour-based signals take over, and we design the system to improve gradually.
How will we know it works?
By running a controlled test where some visitors see recommendations and others do not, then comparing clicks and orders. Small stores may need longer to reach a clear result, and we say so.
Can we control what gets recommended?
Yes. Rules let you exclude items, pin promotions or avoid showing competitors' categories together. The model ranks within the boundaries your business sets.
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Ready to start your Recommendation Engine Development project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.