Personalization Glossary

At PSYKHE, we're obsessed with the why: Why do we like the things we like? Why does one person like a pair of shoes, a sofa, a travel destination, or a song, while someone else does not? Understanding all the factors that drive individual taste, including personality and psychographic data, enables us to create the world's most sophisticated recommendation engine. After all, you can't personalize without personality. Shaping a stellar recommendation strategy is a big job, so to help you grasp what a complete AI personalization blueprint for your business entails, we've collated definitions related to personalization here.

Recommendation Engine

A recommender system, or a recommendation system, or most commonly, a recommendation engine, is software that uses data and machine learning algorithms to recommend the most relevant items to a particular user or shopper by predicting the preference or rank that a user would give to a product. In the past, the art of recommending products would come from a sales associate or personal shopper. Today, algorithms have taken on the task of deciding what product, service, or experience to recommend to consumers. There are three key types of recommendation engines: collaborative filtering, content-based filtering – and a blend of both.