发表机构
Data Science Institute, University of Technology Sydney; New York University Abu Dhabi（悉尼科技大学数据科学研究所; 纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式推荐忽视内容可信度的问题，提出CreGR模型，在令牌化和生成阶段联合处理可信度，通过可信度感知令牌化和不对称掩蔽策略，在保持准确性的同时减少不可信内容推荐。
AI 中文摘要
生成式推荐（GR）用语义ID（即离散的令牌序列）表示项目，并生成目标项目令牌作为推荐。尽管其效果显著，现有方法主要优化准确性，而忽视了所生成推荐的可信度。这一疏忽不可避免地使用户接触到不可信内容（如假新闻），带来严重的社会后果，包括用户不信任、平台声誉受损以及更广泛的社会不稳定。为解决这一关键但未充分探索的挑战，我们提出了CreGR，这是首个可信的GR模型，在GR的两个核心阶段（令牌化和生成）联合处理内容可信度。在令牌化阶段，我们设计了一种新的可信度感知令牌化器，明确鼓励模型为可信和不可信项目分别学习判别性令牌，从而在令牌级别解耦可信度信号。在此基础上，在生成阶段，我们提出了一种基于离散扩散的新型保准确性和可信度导向的生成器。具体来说，我们引入了一种不对称掩蔽概率降低策略，选择性地减少与不可信内容相关的令牌对生成过程的贡献，同时保持编码用户偏好信号的令牌不受影响，以保留推荐准确性。在三个真实世界数据集上的实验证明了CreGR的有效性。
英文摘要
Generative recommendation (GR) represents items with semantic IDs (i.e., discrete token sequences) and generates target item tokens as recommendations. Despite its promising results, existing methods predominantly optimize for accuracy while neglecting the credibility of the recommendations they generate. This oversight inevitably exposes users to uncredible content (e.g., fake news) with serious societal consequences, including user distrust, reputation harm to platforms, and broader social instability. To address this critical yet underexplored challenge, we propose CreGR, the first credible GR model that jointly tackles content credibility across the two core stages of GR: tokenization and generation. In the tokenization stage, we design a new credibility-aware tokenizer that explicitly encourages the model to learn discriminative tokens respectively for credible and uncredible items, thereby disentangling credibility signals at the token level. Building on this, in the generation stage, we propose a novel accuracy-preserving and credibility-oriented generator grounded in discrete diffusion. Specifically, we introduce an asymmetric masking probability reduction strategy that selectively diminishes the contribution of tokens associated with uncredible content to the generation process, while leaving tokens encoding user preference signals unaffected so as to preserve recommendation accuracy. Experiments on three real-world datasets demonstrate the effectiveness of CreGR.
