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.

Deep Learning

A subset of machine learning, deep learning has infiltrated and made its way up every benchmark across most machine learning subfields, such as natural language processing, computer vision, and so on. To achieve this, deep learning uses a multi-layered structure of algorithms called “neural networks”. The key aspect of deep learning is that these layers of neural networks are able to construct additional features in an automatic manner, instead of being manually designed by human engineers. In contrast to traditional machine learning, within ecommerce, deep learning uses an initial set of given features usually used to predict purchases (such as age, or region) to build more abstract and compressed features based on different combinations of those initial features. A growing technological field, deep learning is a core element when developing predictive modeling, building precise recommendation engines, and shaping personalized content.