Abstract: Bitmask encoding-based personalized style generation is described. A personalization system presents different instances of digital content in a user interface and prompts a user to indicate interest or disinterest in an individual digital content items. Based on the input indicating interest or disinterest, a bitmask encoding is generated and returned to the personalization system. The personalization system queries a database to identify respective instances of digital content that correspond to each binary value included in the bitmask encoding, which is then used to generate a prompt that causes a machine learning system to generate a personalized style for the user from which the favorable or unfavorable indications were received. The machine learning system generates a personalized style for an individual and identifies different instances of digital content of interest to the individual, and the personalized style is used to generate a user interface for the individual.