Recommender systems
Recommendation platforms for filtering information

Almost everyone who makes an online purchase is exposed to it: Recommender systems. These systems make recommendations for products in order to deal with the problem of information overload. Recommender systems have the ability to predict whether a particular user would prefer an item based on the user’s preferences, interests, or observed behavior of the user. Recommendations can also be made based on the properties of items or products.

Recommender systems have undergone significant growth in recent years. For an increasing number of companies and organizations, they have proven to be effective means of offering more targeted products to consumers and thus increasing revenue.

Specialized algorithms

At CentERdata we advise companies and organizations on setting up recommender systems. We also develop ready-made solutions for companies to approach their customers in a more focused and effective way (via website, apps, e-mails, or by post). We achieve this with specialized algorithms, such as collaborative, content-based, session-based and hybrid filtering systems.

Example project: Sligro Data Science Lab

We are currently supporting the Sligro Data Science Lab in research into recommender systems. Recommender systems are now central to predictive marketing. They can be utilized in almost every industry to optimize and improve customer experience.

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