Tag: Recommender System

In medio stat virtus? Not always!

The Problem What do you do when the model is underperforming? When the models’ performance does not meet our expectations, we usually spend time searching for the flaws, selecting and analyzing the cases where it failed to understand why it happened. Then, we try to apply more robust solutions, train, test, and repeat. In some […]

Written by on Apr 10, 2023

Local vs. global optimization

Is the fastest route always the best? This article may give you a different perspective if your answer is yes. Normally there are multiple ways to tackle a given problem or task, and the optimization field is no different, as there are different approaches we can take to find an optimal solution. The choice of […]

Written by on Jun 23, 2022

Achieving diverse product recommendations

In this blog post, you’ll learn about some examples of decision processes you can use in recommender systems: do you see any usage for recommending less popular products as a way to improve your business? You will see it now! The Use Case Let’s imagine a use case where you are building a MOOC platform […]

Written by on May 25, 2022

NILG.AI among winners of the DataHub Ruhr Open Innovation call

We are proud to announce that NILG.AI was among the winning startups of the 4th open call for the DataHub Ruhr. DataHub Ruhr is an open innovation initiative that creates matchmaking opportunities between major corporations in the Ruhr area and global startups. In the 4th edition of this program, 8 challenges were proposed by 4 […]

Written by on Jul 17, 2021

An AI-Based Image Content Retrieval System

Similarity measurement is the basis for any information retrieval, management, or data mining system. Both in industry and in the scientific community, similarity detection has been shown to be extremely useful when applied to different use cases. Over time, the information available on the internet has been growing in an exponential way, making it harder […]

Written by on Mar 7, 2021