Recommendations Your Users Act On,
Not Just See
Collaborative filtering, real-time personalisation, and A/B-tested ranking models — we build recommendation systems that surface the right content, product, or action at the right moment, without feeling intrusive.
What we build
End-to-end recommendation system services
From signal audit to real-time serving infrastructure — every layer of a personalisation system that moves business metrics, not just model metrics.
Collaborative Filtering
User-based and item-based collaborative filtering with implicit and explicit feedback signals — matrix factorisation, ALS, and neural collaborative filtering tuned to your interaction data.
Content-Based & Semantic Filtering
Two-tower embedding models and item-feature matching that recommend based on what users engage with, not just who else engaged with it — handling catalogue updates and new items cleanly.
Hybrid Recommendation Systems
Ensemble and cascade architectures that blend collaborative, content-based, and context signals — balancing accuracy, diversity, and novelty across the full recommendation surface.
Real-Time Personalisation
Sub-50ms recommendation APIs backed by pre-computed embeddings, online feature stores, and vector similarity search — so personalisation updates within seconds of a user's latest action.
Ranking & Re-Ranking Models
Learning-to-rank models and contextual bandits that go beyond relevance — optimising for business objectives like revenue per session, watch time, or margin alongside engagement.
A/B Testing & Experimentation
Holdout group design, interleaving experiments, and multi-armed bandit frameworks that measure the real lift from recommendation changes — not just CTR, but downstream business metrics.
Cold Start Strategies
Popularity baselines, onboarding preference capture, content-based bootstrapping, and exploration policies that give new users and new items quality recommendations from their very first interaction.
Recommendation Infrastructure
Feature pipelines, model registries, online/offline serving layers, and A/B routing infrastructure — the engineering that makes recommendation models deployable, versioned, and maintainable at scale.
Monitoring & Continuous Improvement
Click-through rate, diversity, coverage, and novelty dashboards alongside business KPIs — with drift alerts and automated retraining triggers so recommendation quality improves as your catalogue and users evolve.
Where personalisation drives value
Engagement uplift across every product category
Recommendation systems work wherever the right next item, content, or action is non-obvious — and the cost of guessing wrong is a user who leaves.
E-Commerce
Product recommendations that increase average order value — similar items, frequently bought together, and personalised homepage shelves driven by purchase and browse history.
Media & Streaming
Next-video and next-article recommendations that lift watch time and reduce churn — trained on completion rates and session depth, not just clicks.
Marketplaces
Buyer-to-listing matching and seller ranking that surfaces relevant inventory to the right buyer at the right moment in their session.
SaaS Products
Feature discovery, next best action, and in-app content recommendations that increase activation and reduce time-to-value for new users.
EdTech
Personalised learning paths, next-lesson recommendations, and practice problem selection adapted to each learner's pace, gaps, and goals.
News & Content
Personalised content feeds and article recommendations that balance relevance with diversity — keeping users engaged without narrowing into a filter bubble.
30%+
Average engagement uplift
<50ms
Recommendation serving latency
Real-time
Personalisation from first interaction
100M+
Recommendations served daily
Signal Audit & Problem Framing
Step 01Map your available interaction signals — clicks, purchases, watch time, ratings — and define the business metric the recommendation system is being optimised to move.
Baseline & Algorithm Selection
Step 02Start with a strong, measurable baseline before adding complexity — popularity models, simple collaborative filtering — so every upgrade is justified by measured lift.
Feature Engineering & Embeddings
Step 03Build user and item representations from interaction history, content features, and context signals — the embedding quality determines the ceiling of every downstream model.
Offline Evaluation & Online Test
Step 04Measure precision@k, NDCG, and coverage offline, then A/B test against the baseline in production — business metrics decide what ships, not model metrics alone.
Serving Infrastructure & Monitoring
Step 05Deploy low-latency serving with pre-computed candidates and online re-ranking, instrument engagement and business KPIs, and build the retraining loop that keeps quality from degrading.
Ready to make your product smarter about what to show next?
Tell us your product type and available interaction signals — we'll scope a recommendation architecture and have a baseline model running within two weeks.
Technologies
Our recommendation systems stack
TF Recommenders
Modelling
PyTorch
Modelling
Apache Spark
Data
Faiss
Vector
Redis
Serving
Feast
Features
MLflow
Tracking
Kafka
Streaming
AWS Personalize
Cloud
Python
Language
FastAPI
Backend
PostgreSQL
Storage
Why mabzone Recommendations
What makes our recommendation systems work in production
Measured lift on business metrics, real-time serving, and cold start handled — not a model that looks good in a notebook.
Offline and online evaluation, not just intuition
We measure precision, recall, and diversity offline, then validate with A/B tests in production — a recommendation system isn't considered working until the business metric moves.
Real-time serving designed in from the start
Latency is an architectural decision, not an afterthought. We design the candidate generation and re-ranking pipeline for your serving budget before a single model is trained.
Cold start solved before launch
New users and new items need recommendations from day one. We build explicit cold start strategies — not a fallback to 'show popular items' that you'll fix later.
Business metrics drive every decision
CTR is not the goal. We align the recommendation objective to revenue, engagement depth, or retention — and measure whether the system actually moves those numbers.
Privacy and filter-bubble aware by design
We build diversity and serendipity signals into the ranking layer and design data pipelines around consent and data minimisation — so the system is defensible to regulators and users.
Compliance & Standards
Compliance Standards That Shape Our Recommendation Systems Practice
We build personalisation systems within the privacy, transparency, and algorithmic accountability frameworks that users and regulators increasingly require.
GDPR (EU)
Lawful processing of behavioural and preference data for personalisation
CCPA
Consumer data rights and opt-out controls for recommendation data processing
EU AI Act
Transparency obligations for algorithmic recommendation systems
Digital Services Act
Recommender system transparency and user control requirements for large platforms
ePrivacy Directive
Consent requirements for behavioural tracking and personalisation cookies
COPPA
Child data protection for recommendation systems serving users under 13
NIST AI RMF
Risk management framework for trustworthy algorithmic recommendation
ISO 27001
Information security management for user data and recommendation infrastructure
SOC 2 Type II
Security and availability controls for personalisation platforms
IEEE 7000
Ethical design standards for recommendation systems affecting user behaviour
Recommendation Engine FAQs
Common questions about building personalisation systems for production products.
Let's Build Together
What should your product be recommending that it isn't yet?
Tell us your product and available signals — we'll have a baseline recommendation model running within two weeks.