Personalization engines are a type of information filtering system that can be applied on virtually any kind of website and help users find new content that is relevant to their interests – from music and events to products and dating profiles.
Yusp automatically learns and analyses the browsing and/or shopping behavior of the user on a website or platform. The system collects data from other users and items and compares this data to create a list of recommendations that are personally relevant to the visitor’s taste.
Yusp uses collaborative filtering and matrix factorization techniques – some of them patented in the US – in order to learn the taste of the user, constantly enhancing the quality of recommendations.
Personalization engines are a better alternative to search algorithms, since they’re able to guide visitors to items or content that they didn’t even know they were looking for.