Retail & E-commerce
Personalization That
Actually Works
Real-time recommendations that update as users browse. The ML platform powering personalization at Europe's leading retailers.
Use Cases
From product discovery to inventory optimization — ML across the retail value chain
Personalized Recommendations
Real-time product suggestions based on browsing behavior, purchase history, and user preferences. Update recommendations as users interact.
TikTok-like personalization
Search & Ranking
Personalized search results that understand user intent. Combine semantic search with user preferences for better discovery.
Two-tower retrieval models
Dynamic Pricing
Optimize prices in real-time based on demand, inventory levels, competitor pricing, and customer segments.
Real-time price optimization
Demand Forecasting
Predict demand at SKU level to optimize inventory, reduce waste, and prevent stockouts.
Reduce inventory costs
Customer Segmentation
Dynamic customer segments based on behavior, not just demographics. Power targeted marketing campaigns.
Behavioral segmentation
Churn Prediction
Identify at-risk customers before they leave. Trigger retention campaigns with real-time propensity scores.
Proactive retention
4-Stage Recommender Architecture
How modern recommendation systems work at scale
Candidate Retrieval
Vector similarity search retrieves thousands of potentially relevant items from millions using embeddings.
Two-tower models + VectorDB
Feature Enrichment
Enrich candidates with real-time features: user history, item popularity, contextual signals.
Feature Store (<1ms)
Personalized Ranking
Ranking model scores candidates using user features, item features, and context.
Neural ranking models
Feedback Loop
User interactions feed back to improve embeddings and ranking. Continuous learning.
Online learning
User
VectorDB
Candidate Retrieval
Feature Store
Enrichment
Ranking Model
Personalization
Results
Why Retail Personalization Is Hard
Challenges that Hopsworks is built to solve
Personalization at Scale
Millions of users, millions of products. Computing personalized recommendations for every user in real-time requires serious infrastructure.
Data Freshness
User preferences change by the minute. Batch recommendations are stale by the time they're served. Real-time is the new baseline.
Cold Start Problem
New users have no history. New products have no interactions. Models need strategies to handle sparse data.
Training-Serving Skew
Models trained on yesterday's features fail when served different data in production. Feature consistency is critical.
Built for Retail Scale
Everything you need for production recommendation systems
Real-Time Features
Sub-millisecond feature retrieval. Update recommendations as users browse.
Vector Database
Built-in embedding store for candidate retrieval. No separate vector DB needed.
Online Learning
Continuous model updates from user feedback. Always improving.
Feature Consistency
Same features for training and serving. No skew, ever.
A/B Testing
Built-in experiment tracking. Compare model versions in production.
MLOps
Model registry, versioning, deployment pipelines, monitoring.
<1ms
Feature retrieval
50M+
Users at Zalando
25
Countries served
Real-time
Personalization
Customer Stories
Powering personalization at leading retailers
FASHION E-COMMERCE
Global Fashion Retailer
Europe's leading online fashion platform powers real-time personalization across 25 countries and 50 million customers with Hopsworks.
Real-time product recommendations
Personalized search ranking
Dynamic customer segmentation
"Hopsworks was thoroughly evaluated against several other Feature Store vendors, examining governance, performance, support level and stability."
Real-Time Search & Recommendations
Deep dive into retrieval and ranking
Learn More
Explore tutorials and courses on building real-time recommendation systems.
Stop Showing Generic Recommendations
Real-time personalization that updates with every click. See how Hopsworks powers recommendations at scale.