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

Jan. 24^th, 2024: Got accepted by TheWebConf ‘24.

Qijiong LIU, Lu FAN^*, Jiaren XIAO^*, Jieming ZHU, and Xiao-Ming WU^#
*Equal contribution (co-second authors).
[Code] [Paper]

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.

Citation

@inproceedings{liu2024cage, 
    title = {Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization},
    author = {Liu, Qijiong and Fan, Lu and Xiao, Jiaren and Zhu, Jieming and Wu, Xiao-Ming},
    booktitle = {Proceedings of the ACM Web Conference 2024},
    month = {may},
    year = {2024},
    address = {Singapore}
}