User simulation aims to reproduce how users interact with content and products across online platforms. Recent generative approaches have enriched user simulation with behavioral history and contextual information. However, realistic browsing simulation still faces two challenges. (i) Stochastic Responses: Even with the same recommendation feed, the same user may produce different browsing responses, yielding distinct behavioral trajectories. A logged trajectory records only one such realization. (ii) Sparse Key Events: Clicks and likes provide important feedback but occur sparsely within browsing trajectories dominated by progression actions such as scrolling. Many stepwise simulators predict these actions in a dense sequence, where key events contribute relatively few training targets. To address these challenges, we propose BrowseFlow, a conditional discrete flow matching method for generative user simulation. For Sparse Key Events, BrowseFlow represents each response by its browsing extent and an ordered sequence of key events, directly modeling their counts and locations under exposure constraints. BrowseFlow learns the conditional distribution of complete browsing responses through probabilistic extent prediction and discrete flow matching over key events. Random event retention defines probability paths over partial event sequences, while transition rate matching learns stochastic insertion dynamics. For Stochastic Responses, we use the energy score, a proper scoring rule, to evaluate response fidelity and generated variability, complemented by the Wasserstein distance between key event count distributions. We further introduce TaobaoBrowse, a benchmark pairing real recommendation contexts with fine-grained browsing trajectories. Experiments across three datasets demonstrate that BrowseFlow consistently achieves the best distributional performance.

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