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起着关键作用。然而，在基于ID的推荐场景中，物品类别信息并非始终可用。本文提出了一种新方法，能够自动学习并生成面向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分的向量量化框架CAGE，用于自动生成类别树。该框架能够从随机初始化状态开始，以端到端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可以轻松集成到顺序推荐系统和非顺序推荐系统中。我们通过在多种推荐任务（包括列表补全、协同过滤和点击率预测）及不同推荐模型上的实验，验证了CAGE的有效性。我们已公开代码和数据，以供他人复现所报告的结果。