Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization

TL;DR

This paper introduces CAGE, a differentiable vector quantization method that automatically learns entity category trees, improving ID-based recommendation systems by enhancing their ability to incorporate category information.

Contribution

The paper proposes a novel end-to-end framework, CAGE, for automatic category tree learning in ID-based recommendation, adaptable to various models and tasks.

Findings

CAGE effectively learns category trees that improve recommendation accuracy.

CAGE can be integrated into multiple recommendation models with ease.

Experimental results show improved performance across tasks.

Abstract

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.