Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

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 …

In recent years, generative diffusion models have achieved a rapid paradigm shift in deep
generative models by showing groundbreaking performance across various applications …

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the
informative uncertainty for recommendation. Considering the difference between …

Sequential recommender systems often suffer from performance drops due to the data-
sparsity issue in real-world scenarios. To address this issue, we bravely take advantage of …

Collaborative filtering is a critical technique in recommender systems. It has been
increasingly viewed as a conditional generative task for user feedback data, where newly …

A recent study has shown that diffusion models are well-suited for modeling the generative
process of user--item interactions in recommender systems due to their denoising nature …

Inspired by the power of Diffusion Models (DM) verified in various fields, some pioneering
works have started to explore DM in recommendation. However, these prevailing endeavors …

Generative information retrieval (Gen-IR) is a fast-growing interdisciplinary research area
that investigates how to leverage advances in generative Artificial Intelligence (AI) to …