Recommender Systems

Providing the right content to the right customer at the right time requires performant recommender systems. Leverage to increase your capacity to process large amounts of data to make the best recommendations on any application.


What is a Recommender System?

Recommender systems rely on an information filtering technique based on collecting and analyzing historical data from users’ behaviors, activities, preferences and predicting what they will like. Those systems are widely used in banking, e-commerce, retail, and media industries to recommend products or contents to users. Yet, most recommender systems are not truly predictive as they are mainly based on users similarity to other users. This represents a missed opportunity for many organizations that fail to benefit from predictive recommendation engines.


Making More Personalized Recommendations

With the arrival of artificial intelligence, it is now much more accessible for companies to leverage machine learning algorithms to make personalized recommendations. Such models use the same principle of extracting information from user data but use a mix of content-based recommendation and collaborative filtering algorithms to get better results. This drives personalization and helps to answer customers ever-increasing expectations.

Using for Recommender Systems provides teams with an automated platform to quickly build and deploy machine learning models in any application (website, mobile app, ... ). The platforms’ intuitive interface makes the task of building performant recommendation engines much more accessible. Start making better predictions and engage your customers with the product or content that will resonate with them.

The platform makes my work exciting, fun and provides me with more accurate and timely results - it's almost magical!

Program Director - French Radio Antenna

Start using predictive intelligence for your operations