Personalized Content Generation as Preference Distribution Completion

Personalized content generation is becoming increasingly important in modern generative systems. Existing approaches typically personalize provided prompts, instructions, or references; directly generating content from user behavior requires inferring preferences from limited observations. This makes it difficult to determine both what to generate and how generation probabilities should be distributed across interests. In this paper, we formulate the personalized content generation task as preference distribution completion. To this end, we develop a collaborative framework that draws on cross-user behavioral patterns to supplement sparse individual preferences. The framework combines source-distribution modeling with history-conditioned Riemannian flow matching, which together determine how probability mass is allocated in a semantic latent space. A decoder maps samples from the resulting distributions directly to content, without explicit user prompts. Experiments on short-video user interaction benchmarks, with textual descriptions as the generation targets, demonstrate the effectiveness of our approach.