With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative
filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …

With the increasing popularity of posting multimodal messages online, many recent studies
have been carried out utilizing both textual and visual information for multi-modal sarcasm …

Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism which …

Recent methods in sequential recommendation focus on learning an overall embedding
vector from a user's behavior sequence for the next-item recommendation. However, from …

Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …