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质量与个性化方面具有关键作用。然而，在基于ID的推荐场景中，项目类别信息的可用性并不总是存在。本文提出一种新方法，能够自动学习并生成面向ID推荐的实体（即用户或项目）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，该框架支持从随机初始状态出发，以端到端的方式同时学习与优化类别编码表示和实体嵌入。凭借其高度适应性，CAGE可轻松集成至序列与非序列推荐系统中。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）以及不同推荐模型上验证了CAGE的有效性。我们已公开代码与数据，供他人复现报告结果。