With the continuous increase of users and items, conventional recommende...
Current learning-based edge caching schemes usually suffer from dynamic
...
Knowledge graphs (KGs), containing many entity-relation-entity triples,
...
Maximizing the user-item engagement based on vectorized embeddings is a
...
Recommender systems now consume large-scale data and play a significant ...
Personalized recommender systems have been widely studied and deployed t...
Representation learning has been a critical topic in machine learning. I...
Diversifying search results is an important research topic in retrieval
...
Model quantization enables the deployment of deep neural networks under
...
The security of artificial intelligence (AI) is an important research ar...
To offer accurate and diverse recommendation services, recent methods us...
Implicit feedback is frequently used for developing personalized
recomme...
Bi-level optimization, especially the gradient-based category, has been
...
Learning vectorized embeddings is at the core of various recommender sys...
Implicit feedback is widely leveraged in recommender systems since it is...
Prior research on exposure fairness in the context of recommender system...
One major problem in black-box adversarial attacks is the high query
com...
Two-sided marketplaces are an important component of many existing Inter...
Reasoning in a temporal knowledge graph (TKG) is a critical task for
inf...
Personalized recommender systems are increasingly important as more cont...
Personalized recommender systems are playing an increasingly important r...
We propose a simple and highly query-efficient black-box adversarial att...
Many adversarial attacks have been proposed to investigate the security
...
Building compact convolutional neural networks (CNNs) with reliable
perf...
Transfer in Reinforcement Learning (RL) refers to the idea of applying
k...
Personalized recommendation is ubiquitous, playing an important role in ...
The chronological order of user-item interactions can reveal time-evolvi...
Despite its potential to improve sample complexity versus model-free
app...
Latent Dirichlet Allocation (LDA) model is a famous model in the topic m...
Deep neural networks (DNNs) are vulnerable to adversarial attack which i...
The chronological order of user-item interactions is a key feature in ma...
Modeling action units (AUs) on human faces is challenging because variou...
The rapid growth of Internet services and mobile devices provides an
exc...
The rapid growth of Location-based Social Networks (LBSNs) provides a gr...
The objective of transfer reinforcement learning is to generalize from a...