This paper describes a self-supervised framework called BUMP, which is designed to improve how large language models deliver personalized content. Traditionally, creating user profiles for search and recommendation tasks requires expensive, human-labeled data to train the system. To solve this, researchers developed a method that uses a bidirectional ranking objective to learn directly from raw interaction logs without manual supervision. By comparing a user's generated profile against their actual history, the system creates a dense reward to refine the model's accuracy. This approach allows the AI to summarize interaction histories into natural language descriptions that are as effective as those produced by more costly, supervised methods. Ultimately, the source demonstrates that personalization can be achieved efficiently by training models to recognize the unique patterns in a user's own digital footprint.