Recommendation banner
Recommendation mbanner

Home > Nia > Case Studies > Recommendation


A leading handset manufacture was looking to monetize content delivery and built out a mobile video streaming service. They wanted to promote additional sales/rental of movies and wanted to tune their legacy recommendation engine running on an older version of Hadoop with Mahout. Major pain points were the speed and accuracy of their existing approach, despite having a large team of modelers within the company.


We built a machine learning model using our recommender system. In less than a week, the solution delivered a 1500x improvement in run-time and improved the accuracy of the model by 20%. The model provides an interpretable system that optimally understands user preferences.


  • 20% improvemen t in recommendation relevance

  • 1500x execution speedup

  • Optimally understand user preferences

  • Allow for diversity in recommendation without excessive obscurity

Case Studies

Real-Time-Fraud-Detection cs banner

Real-Time Fraud Detection

A major global financial company was looking to detect fraudulent transactions in real time. They were looking to block these transactions or notify the customers immediately, thereby avoiding the...

Data-Center-Optimization cs thumb

Data Center Optimization

A data center for a major payments company had high equipment costs. There was a huge and rapidly growing machine data volume from thousands of feeds, leading to capital...

Customer-Acquisition-cs thumb

Customer Acquisition Via Micro-Targeting

A Fortune 100 financial customer was looking to do a micro-targeting marketing application to extend high end credit card offers to high net-worth individuals. They were using a legacy...