The recent" pre-train, prompt, predict training" paradigm has gained popularity as a way to
learn generalizable models with limited labeled data. The approach involves using a pre …

The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

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 (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the user's historical …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

Ratings of a user to most items in recommender systems are usually missing not at random
(MNAR), largely because users are free to choose which items to rate. To achieve unbiased …

Personalized recommender systems play a crucial role in capturing users' evolving
preferences over time to provide accurate and effective recommendations on various online …

With the rapid evolution of transformer architectures, researchers are exploring their
application in sequential recommender systems (SRSs) and presenting promising …

With the explosive growth of commercial applications of recommender systems, multi-
scenario recommendation (MSR) has attracted considerable attention, which utilizes data …

Sequential recommender systems (SRSs) aim to suggest next item for a user based on her
historical interaction sequences. Recently, many research efforts have been devoted to …