Practice or pre-training period for a user to gain familiarity with the system and vice-versa
Recommender Systems・Autonomous Vehicles・Consumer Products・Personalization

Work In Progress

Our Elements guide is still in progress, and therefore lacks full visual and technical assets. We hope to release them by summer of 2020. Thanks for reading Lingua Franca!


A Warm-Up is a cross between a tutorial, an information-gathering phase, and a practice run. Unlike typical interfaces where diagrams can introduce the user to the experience, AI systems often require a small amount of user data to kick-start their personalization. Instead of shying away from this, design should lean in to this requirement, giving the user a special set of user interactions that helps them get acquainted with the system and explore its capabilities to find a style of interaction that suits them.


There exists a pervasive challenge with recommendation algorithms called the cold-start problem, which describes the precisely opposite phenomenon from the warm-up. In a cold-start scenario, the algorithm provides low-quality recommendations because it lacks any context or experience from past user interactions. When interactions between the user and these low-quality recommendations are measured (for use as future training data), the AI will often generalize incorrectly (e.g. assuming the user doesn’t want any clothing when the user rejectes a few recommended clothes). This can create a negative feedback spiral leading to both an awful user experience and bad data for further learning.

Instead, the warm-up attempts to create a stronger foundation for your system. You can provide the user with a few known examples to judge (that perhaps all users see initially), allowing the system to easily discern the user’s tastes as compared to others’. By leading the user through a few initial recommendations as well as the process or accepting or rejecting them, they are also trained on using a dynamic UI.