I study the role of ambiguity preferences in predicting take-up of algorithmic (i.e., personalized) recommendations. In a two-part experiment, I first elicit individuals's preferences towards risk and ambiguity. In Part 2, I ask participants to make choices over a menu of risky prospects after I provide a recommendation (or not) based on their treatment assignment. I assign individuals to one of two control groups (no recommendation or randomly created recommendation) or to one of two treatment groups (personalized recommendation with no information or personalized recommendation with information). The difference between the two treatments is only the information provided to individuals on how the recommendation is constructed; both recommendations are of similar quality (i.e., based on the individual's risk preferences elicited in Part I).