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cs.IR2026
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu +2
In recommendation systems, users interact with only a small fraction of a vast item catalog, producing feedback that is both sparse and noisy. This challenges post-training generat…
cs.IR2026
Towards Generalizable and Efficient Large-Scale Generative Recommenders
Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya
Generative recommendation models can model user behavior as sequences of events and provide a shared backbone for multiple recommendation tasks. In production, however, pre-trainin…
cs.IR2024
POSIT: Promotion of Semantic Item Tail via Adversarial Learning
Qiuling Xu, Pannaga Shivaswamy, Xiangyu Zhang
In many recommendations, a handful of popular items (e.g., movies / television shows, news, etc.) can be dominant in recommendations for many users. However, we know that in a larg…