Work

Hyperbolic Diffusion Recommender Model

Year: 2025

Type: conference-paper

Abstract: Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities between images and items. Consequently, items often exhibit distinct anisotropic and directional structures that are less prevalent in images. However, the traditional forward diffusion process continuously adds isotropic Gaussian noise, causing anisotropic signals to degrade into noise, which impairs the semantically meaningful representations in recommender systems.

Source: International World Wide Web Conference (WWW)

DOI: 10.1145/3696410.3714873

Authors: Meng Yuan, Yutian Xiao, Wei Chen, Chou Zhao, Deqing Wang, Fuzhen Zhuang

Institutions: Beihang University, Chongqing University of Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities

Awards: G245388120, G5191158251, G6617559040