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In the article we will present the concrete result coming from experiments on real-world data.

The article covers the basics of recommendation models and mainly explain the session-based approach on examples though several algorithms.

We’re going to describe more details about the experiment itself. What kind of dataset has been used, discuss its specifics and characteristics, which performance metrics have been chosen for final comparison, show final results and in the end, present final conclusions.

Recommendation systems are nowadays used almost everywhere and there is no doubt they can help users to choose the right product. From a business perspective, they increase business metrics and, in the end, earn money.

Usually, in my posts, I focus on a specific use case and focus on its detail. This time I decided to give you an overview of the most frequent use cases and business issues where Zoe.AI can help.

In my last post dedicated to using cases in insurance and how Zoe.ai can help to strengthen the investigation process, I mentioned 10% of lying users while filling in an online form. This problem concerns an online loan application process too.

Create a unique biometric profile and enable the behavioral prescoring.

What does it mean for you?