Recommendation Engine Development
A good recommendation feels like a helpful shop assistant. A bad one shows you what you already bought. We build engines that learn from real behaviour, cope with new users and items, and are tested against a control.
In short. Recommendation Engine Development is one of Matrida Infotech's AI services. We build recommendation systems that learn from behaviour and preference, and prove their lift with controlled tests. Typical stack: Scikit-learn · TensorFlow Recommenders · Amazon Personalize · feature store · A/B testing.
Sound familiar?
- Shoppers or readers leave without finding what suits them
- Your "popular items" list is the same for everyone
- New users and new items get no recommendations
- You cannot show that personalisation raised revenue
Capabilities
- Collaborative filtering
- Content-based filtering
- Machine learning recommendations
- AI-driven personalisation
- Real-time recommendations
What we build
- Collaborative filtering that learns from what similar people chose
- Content-based models that use item attributes for new products
- Hybrid models that mix both and handle cold starts
- Real-time ranking that reacts to the current session
- Experiment setup to measure lift against a control group
What you get
- Recommendation service with an API
- Offline and online evaluation report
- A/B test design and tracking
- Monitoring for drift and fairness
Tools and frameworks
- Scikit-learn
- Surprise
- Apache Mahout
- TensorFlow Recommenders
- Amazon Personalize
Where it fits: Retail & E-commerce.
Questions
How much data do we need?
Enough interactions per item and per user to learn patterns. Small catalogues can start with content-based and popularity-with-context methods, then add collaborative signals as data grows.
How do you handle new users and new products?
With content features, onboarding signals and exploration, so new items get a fair chance to be seen.
How do you prove it works?
With an A/B test against your current experience, measuring the outcome you care about, such as conversion, revenue per visit or time to find an item.
Can it explain why something was recommended?
Yes, where the method allows it. Simple reasons such as "because you viewed X" build trust and help debugging.
Does it respect privacy?
We design around consent, minimal data and your local data protection rules, and can run without personal identifiers where that is enough.
Related services
AI Agent Development
We build AI agents that complete multi-step work inside your systems, with guardrails, human approval and full audit logs.
Learn more →RAG & Enterprise Knowledge Assistants
Private assistants that answer from your documents with citations, permission-aware retrieval and measured accuracy.
Learn more →Generative AI Application Development
Customer-facing and internal LLM products, from document drafting to AI copilots inside your SaaS.
Learn more →Planning Recommendation Engines? Tell us the workflow.
Say what is going wrong today. We reply with how recommendation engine development would handle it, a rough scope and the first thing to build.
- You would get: recommendation service with an API
- You would get: offline and online evaluation report
- You would get: a/B test design and tracking