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All too often companies have only the vaguest idea about what kind of data they’re holding; because such data is very often hidden deeply away in a variety of databases and fragmented across different departments. We identify this data and bring it to light, making it visible, cohesive, comparable and easy to understand so that it really does support YOU in making the right decisions. And if need be, we can also identify any lacking data and define a concept to fill in the gap.

Developing a Recommendation Engine using Hybrid Approach

Posted by on Aug 22, 2017 in Case Studies | No Comments

 

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Client: A cloud-based interactive smart video platform that delivers personalized interactive videos on-demand to consumers.

Objective

Business: Increase time spent on site, number of videos watched, and repeat visits.

The Engine: Devise an engine that offers relevant recommendation dynamically and in real-time.

The Need

Real-time recommendations need to be self-learning and based on massive amounts of data getting collected on a continuous basis.

The chosen solution had to ensure:

  • fault tolerance along with low latency
  • scalability and the effective deployment

Modelling

The model was trained using the video meta data – genre, ratings, tags etc. as well as the user current viewing behaviour/history.

Deployment

The solution was integrated with the client’s data stores and exposed to developers using command line interfaces and REST APIs.

 

Venugopala Rao Manneni

A doctor in statistics from Osmania University. I have been working in the fields of data analysis and research for the last 14 years. My expertise is in data mining and machine learning – in these fields I’ve also published papers. I love to play cricket and badminton.

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