Generative models are increasingly used in recommender systems, both for modeling user behavior as event sequences and for integrating large language models into recommendation pipelines. A key challenge in this setting is the extremely large cardinality of item spaces, which makes training generative models difficult and introduces a vocabulary gap between natural language and item identifiers. Semantic identifiers (semantic IDs), which represent items as sequences of low-cardinality tokens, have recently emerged as an effective solution to this problem. However, existing approaches generate semantic identifiers of fixed length, assigning the same description length to all items. This is inefficient, misaligned with natural language, and ignores the highly skewed frequency structure of real-world catalogs, where popular items and rare long-tail items exhibit fundamentally different information requirements. In parallel, the emergent communication literature studies how agents develop discrete communication protocols, often producing variable-length messages in which frequent concepts receive shorter descriptions. Despite the conceptual similarity, these ideas have not been systematically adopted in recommender systems. In this work, we bridge recommender systems and emergent communication by introducing variable-length semantic identifiers for recommendation. We propose a discrete variational autoencoder with Gumbel-Softmax reparameterization that learns item representations of adaptive length under a principled probabilistic framework, avoiding the instability of REINFORCE-based training and the fixed-length constraints of prior semantic ID methods.

翻译：生成模型在推荐系统中的应用日益广泛，既用于将用户行为建模为事件序列，也用于将大语言模型整合至推荐流程中。在此场景下，一个核心挑战在于物品空间的基数极其庞大，这导致生成模型训练困难，并在自然语言与物品标识符之间形成词汇鸿沟。语义标识（semantic IDs）——将物品表示为低基数标记序列的方法——近期已成为解决该问题的有效方案。然而，现有方法生成的语义标识均为固定长度，为所有物品分配相同的描述长度。这种做法效率低下，与自然语言特性不匹配，且忽略了现实世界商品目录中高度倾斜的频率结构：热门物品与罕见长尾物品本质上具有不同的信息需求。与此同时，涌现通信领域的研究关注智能体如何发展离散通信协议，其过程常产生变长消息，其中高频概念获得更短的描述。尽管存在概念相似性，这些思想尚未在推荐系统中得到系统性应用。在本工作中，我们通过引入用于推荐的变长语义标识，搭建起推荐系统与涌现通信之间的桥梁。我们提出一种采用Gumbel-Softmax重参数化的离散变分自编码器，该模型可在概率框架下学习自适应长度的物品表示，既避免了基于REINFORCE训练的不稳定性，也突破了现有语义标识方法的固定长度限制。