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.

Collaborative Filtering

A common approach for executing product recommendations using machine learning, collaborative filtering looks at the collective set of preferences across users and items to learn from users that have similar behavior patterns. Recommendation systems collect both explicit data actively provided by users (such as numeric rating) and implicit data inferred by the system based on a user’s behavior (such as a preference for a certain product after viewing similar ones in the past). These massive datasets allow systems to craft predictions and serve relevant product recommendations tailored to users’ individual affinities and shopping behaviors.