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 index. Based on theoretical analyses, we identify that the severe popularity bias emerges from the confluence of a token-level optimization flaw and the undifferentiated property of item tokenization. Accordingly, this study develops a novel generative recommender system, called Ghost, by designing the asymmetric unlikelihood optimization and the skeleton-founded tokenization. Extensive empirical evaluations across three datasets, alongside multiple SOTA baselines, reveal that Ghost substantially alleviates popularity bias and promotes fairer recommendations, while incurring slight degradation to the overall recommendation utility.

翻译：摘要：近年来，以统一端到端框架为特征的生成式推荐系统（GRs）在革新推荐范式方面展现出惊人潜力。尽管其效果显著，我们仍发现GRs易受长期存在的推荐社区普遍问题——流行度偏差的影响。虽有少量研究尝试将传统去偏方法拓展至GRs，但其效果有限，且GRs受流行度偏差困扰的根本原因仍未得到深入探讨。为填补这一空白，本研究聚焦GRs的两个核心环节：生成式框架的优化与基于语义索引的项目分词。基于理论分析，我们识别出严重流行度偏差源自分词级优化缺陷与项目分词无差异化属性的共同作用。据此，本研究通过设计非对称非似然优化与骨架导向分词技术，开发了名为Ghost的新型生成式推荐系统。在三个数据集及多种SOTA基准上的广泛实证评估表明，Ghost在轻微降低整体推荐效用的同时，显著缓解了流行度偏差并促进了更公平的推荐。