发表机构
Virginia Tech; Amazon; Meta AI; Google; Dartmouth College（弗吉尼亚理工大学; 亚马逊; Meta AI; 谷歌; 达特茅斯学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式推荐中LLMs与物品索引的错位及长尾物品建模难题，提出HypRQ-VAE框架，在双曲空间学习物品索引，提升了推荐尤其是尾部物品推荐的性能。
AI 中文摘要
序列推荐系统将用户行为建模为物品ID序列，而近期的生成式方法将推荐任务转化为使用大型语言模型（LLMs）的语言建模任务。尽管该范式融入了丰富的文本语义，但存在根本性不匹配：LLMs处理文本 token，而推荐系统依赖离散的物品索引，这种错位常导致生成式推荐出现幻觉。现有方法试图通过在欧氏空间学习物品词汇表来弥合差距，但难以建模现实目录固有的长尾分布——少量头部物品占主导，大量尾部物品反映用户的小众偏好。为解决该问题，我们提出双曲残差量化变分自编码器（HypRQ-VAE），这是首个在双曲空间学习物品索引的框架。HypRQ-VAE利用双曲几何的独特性质，其指数级体积扩张自然适配用户-物品交互的幂律结构，使模型能编码丰富的文本语义，同时保留稀疏长尾物品的表示保真度。在三个基准数据集上的实验表明，HypRQ-VAE显著提升了推荐性能，尤其在尾部物品推荐上表现突出。我们的分析将这些提升归因于双曲空间在生成式推荐中建模物品层次结构和稀疏性的卓越能力。我们的代码和数据可在该https URL获取。
英文摘要
Sequential recommender systems model user behavior as item ID sequences, while recent generative methods cast recommendation as a language modeling task using large language models (LLMs). While this paradigm incorporates rich textual semantics, it introduces a fundamental mismatch: LLMs operate on text tokens, whereas recommender systems depend on discrete item indices. This misalignment often leads to hallucinations in generative recommendations. Existing methods attempt to bridge this gap by learning item vocabularies in Euclidean space, but they struggle to model the inherent long-tail distribution of real-world catalogs, where a small number of head items dominate, and a vast number of tail items reflect users' niche preferences. To address this issue, we introduce Hyperbolic Residual-Quantized Variational AutoEncoder (HypRQ-VAE), the first framework to learn item indexing in hyperbolic space. HypRQ-VAE leverages the unique properties of hyperbolic geometry, whose exponential volume expansion naturally accommodates the power law structure of user-item interactions. This allows the model to encode rich textual semantics while preserving the representational fidelity of sparse, long-tail items. Experiments on three benchmark datasets show that HypRQ-VAE significantly improves the performance of recommendation, particularly in recommending tail items. Our analysis attributes these gains to the superior capacity of hyperbolic space to model item hierarchies and sparsity in generative recommendation. Our code and data are available at: https://github.com/wulongfeng/HypRQ-VAE.
CommentsAccepted for publication in the 2026 IEEE International Conference on Data Mining (ICDM 2026)
