RecDataAgent: User-Conditioned Data Augmentation for Sequential Recommendation

Improving sequential recommendation requires not only learning from user behavior, but also deciding how that behavior becomes training supervision. With heterogeneous preferences, the value of an augmented view depends on whose history it represents and which signals it preserves. We therefore frame data augmentation as the optimization of supervision construction: user selection, method assignment, and exposure form a complete policy whose value is determined by the recommendation outcomes of the resulting corpus. We introduce RecDataAgent, an agent that connects user understanding, executable data construction, and outcome-guided revision. It analyzes train-visible user context to propose augmentation policies, materializes them while preserving task targets and the construction budget, and retains each policy together with its execution trace and measured outcomes. This feedback allows both user partitions and method assignments to evolve, making data construction a decision process informed by the learning it supports. Experiments across sequential recommendation domains show that contextual assignment improves retrieval and ranking under matched method exposure, while outcome feedback strengthens policies beyond user analysis and execution checks. These findings support a shift from designing augmentation methods in isolation to optimizing how they jointly shape supervision for different users. RecDataAgent makes this shift concrete through a closed loop in which user context guides construction and recommendation outcomes guide its revision. Anonymous code is available at https://anonymous.4open.science/r/RecDataAgent-382D.