Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in
the task of sequential recommendation, which aims to capture the dynamic preference …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

Session-based recommendation aims to predict items that an anonymous user would like to
purchase based on her short behavior sequence. The current approaches towards session …

Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …

Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …

Sequential recommendation has become increasingly essential in various online services. It
aims to model the dynamic preferences of users from their historical interactions and predict …

Self-supervised learning with masked autoencoders has recently gained popularity for its
ability to produce effective image or textual representations, which can be applied to various …

Leading sequential recommendation (SeqRec) models adopt empirical risk minimization
(ERM) as the learning framework, which inherently assumes that the training data (historical …

Sequential recommendation systems often suffer from data sparsity, leading to suboptimal
performance. While multimodal content, such as images and text, has been utilized to …