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 …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

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 …

Sequential recommendation (SR) models based on Transformers have achieved
remarkable successes. The self-attention mechanism of Transformers for computer vision …

Self-attention models have achieved the state-of-the-art performance in sequential
recommender systems by capturing the sequential dependencies among user–item …

The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

In Recommender System (RS) applications, reinforcement learning (RL) has recently
emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards …

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 …

With the explosive growth of various commercial scenarios, there is an increasing number of
studies on multi-scenario recommendation (MSR) which trains the recommender system …