arXiv:2509.13648v3 Announce Type: replace Abstract: Generative recommendation plays a crucial role in personalized systems, predicting users' future interactions from their historical behavior sequences. A critical yet underexplored factor in training these models is data augmentation, the process of constructing training data from user interaction histories. By shaping the training distribution, data augmentation directly and often substantially affects model generalization and performance. Nevertheless, in much of the existing work, this process is simplified, applied inconsistently, or trea
The increasing sophistication and widespread deployment of generative AI in personalized systems necessitates more robust data augmentation techniques to improve model generalization and performance.
Improved data augmentation methods for generative recommendation will lead to more effective personalization, enhancing user experience and potentially increasing engagement and revenue for platforms.
A more systematic and effective approach to data augmentation in generative models will likely become a standard, moving beyond simplified or inconsistent previous methods.
- · Personalized recommendation platforms
- · Generative AI researchers
- · E-commerce platforms
- · Content streaming services
- · Platforms using simplistic data augmentation
- · Inefficient personalization systems
Generative recommendation models will become more accurate and robust.
Enhanced personalization will drive higher user engagement and satisfaction across various digital platforms.
The competitive landscape for personalized services will intensify, favoring those with advanced AI data handling.
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