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. Personalization engines are a useful 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. Product recommendation also allows for natural, logical upsell and cross-sell opportunities.
Yusp automatically learns and analyses the browsing and/or shopping behavior of the user on a website or platform. The system collects and analyzes a large amount of information on users’ behaviors, activities or preferences and predicts what users will like based on their similarity to other users.
Yusp uses collaborative filtering, matrix factorization and deep learning techniques in order to learn the taste of the user, constantly enhancing the quality of recommendations.