Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders
Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite their effectiveness, we recognize that GRs are still susceptible to the long-standing issue of popularity bias that has pervaded the recommendation community. Although a few studies have attempted to extend traditional debiasing methods to GRs, their effectiveness is marginal, and the fundamental reason why GRs suffer from popularity bias remains under-explored. To bridge this gap, this study focuses on two core aspects in GRs: the optimization of generative framework and the item tokenization based on semantic ...
| Authors | Jun Yin, Bangguo Zhu, Peng Huo et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 May 2026 |
| Last updated at source | 28 May 2026 |
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