Category information plays a crucial role in enhancing the quality and personalization of recommender systems. Nevertheless, the availability of item category information is not consistently present, particularly in the context of ID-based recommendations. In this work, we propose a novel approach to automatically learn and generate entity (i.e., user or item) category trees for ID-based recommendation. Specifically, we devise a differentiable vector quantization framework for automatic category tree generation, namely CAGE, which enables the simultaneous learning and refinement of categorical code representations and entity embeddings in an end-to-end manner, starting from the randomly initialized states. With its high adaptability, CAGE can be easily integrated into both sequential and non-sequential recommender systems. We validate the effectiveness of CAGE on various recommendation tasks including list completion, collaborative filtering, and click-through rate prediction, across different recommendation models. We release the code and data for others to reproduce the reported results.

翻译：类别信息在提升推荐系统的质量和个性化方面扮演着关键角色。然而，在基于ID的推荐场景中，物品类别信息的可用性并不一致。本文提出了一种新颖的方法，用于自动学习并生成面向基于ID推荐的实体（即用户或物品）类别树。具体而言，我们设计了一个可微的向量量化框架CAGE，用于自动生成类别树。该框架从随机初始状态出发，以端到端的方式同时学习和优化类别编码表示与实体嵌入。凭借其高度适应性，CAGE可轻松集成到序列推荐系统和非序列推荐系统中。我们在多种推荐任务（包括列表补全、协同过滤和点击率预测）上验证了CAGE的有效性，并覆盖了不同的推荐模型。我们公开了代码和数据，以方便他人复现所报告的结果。