Denoising Diffusion Recommender Model

International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 1370–1379

Abstract

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the noise issues from data cleaning perspective such as data resampling and reweighting, but they are constrained by heuristic assumptions. Another denoising avenue is from model perspective, which proactively injects noises into user-item interactions and enhances the intrinsic denoising ability of models. However, this kind of denoising process poses significant challenges to the recommender model's representation capacity to capture noise patterns.

Authors 6

1. Jujia Zhao Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

2. Wenjie Wang

   National University of Singapore

   Affiliation as printed

   National University of Singapore, Singapore, Singapore

3. Yiyan Xu

   University of Science and Technology of China

   Affiliation as printed

   University of Science and Technology of China, Hefei, China

4. Teng Sun

   Shandong University

   Affiliation as printed

   Shandong University, Jinan City, China

5. Fuli Feng

   University of Science and Technology of China

   Affiliation as printed

   University of Science and Technology of China, Hefei, China

6. Tat‐Seng Chua

   National University of Singapore

   Affiliation as printed

   National University of Singapore, Singapore, Singapore