Recommendation Engine

Deep Learning recommendation technology

Why you should care about recommender systems?


A recommendations system is an information filtering technology providing personalized recommendations to users. Recommenders are commonly used for video recommendation systems like Youtube and Netflix or book and product recommendation engines like Amazon and eBay.

Who else should use recommender systems?

Other than VOD, e-commerce and online shopping networks, there are many other online platforms using content and product recommender system to increase their sales. The main purpose of a recommender is to present information on items and products that are likely to be of interest to the user.

Is it worth it to build content and product recommenders?

All the online giants must think it’s worth it, because they are using deep learning recommendation technology. They all understand that if they don’t use powerful collaborative or content-based filtering, they will all be bankrupted very quickly.

YouTube uses Deep Neural Networks for their recommender engine. The company explains: “Deep Learning recently had an immense impact on the YouTube video recommendations system.”

Acting solely on a hunch is no longer necessary, nor is it a good idea in today’s business environment. Using our cutting edge deep learning solutions, you don’t have to rely on chance anymore.


DTVS has a sustainable competitive advantage, because we are working with highly skilled, English & Chinese speaking deep learning specialists in Taiwan.

Our organizational structure and creative environment enables us to develop a recommendation system for a fraction of the costs our competitors charge in the US or Europe.

Getting started: 3 simple steps.

Recommenders are highly complex systems. But, the process to get started is simple. Even a journey of a thousand miles begins with one step.

Data Science Cartoon

By Gregory Piatetsky, KDnuggets.

Step #1: Consulting Services.

Our role as a consultant is generally to provide insights into the recommendation system development process.

Step #2: Exploring the possibilities.

We offer a comprehensive evaluation of the options, based on your unique business situation and the technical specifications of your existing platform.

Step #3: Making an educated decision.

When the options are well understood, we will help you make an educated decision whether or not you are ready for the recommender.

Building the Recommendation System.

If your decision is to build a recommender, we will take the optimal approach to develop a powerful system for you, based on your specific needs.