Recommender systems are typically biased toward a small group of users,
...
As a privacy-preserving method for implementing Vertical Federated Learn...
Federated learning (FL) is a distributed machine learning paradigm that ...
Building a graph neural network (GNN)-based recommender system without
v...
The delayed feedback problem is one of the most pressing challenges in
p...
Federated learning (FL) collaboratively models user data in a decentrali...
Large scale language models (LLM) have received significant attention an...
The increasing concerns regarding the privacy of machine learning models...
Most existing federated learning algorithms are based on the vanilla Fed...
Split learning is a simple solution for Vertical Federated Learning (VFL...
Privacy-preserving cross-domain recommendation (PPCDR) refers to preserv...
Recent regulations on the Right to be Forgotten have greatly influenced ...
In recommendation scenarios, there are two long-standing challenges, i.e...
Spatio-temporal kriging is an important problem in web and social
applic...
Recommender systems are fundamental information filtering techniques to
...
As a practical privacy-preserving learning method, split learning has dr...
Sequential Recommendation (SR) characterizes evolving patterns of user
b...
Integrating multiple online social networks (OSNs) has important implica...
The ever-increasing data scale of user-item interactions makes it challe...
Deep graph learning has achieved remarkable progresses in both business ...
Cross-Domain Recommendation (CDR) has been popularly studied to utilize
...
Privacy laws and regulations enforce data-driven systems, e.g., recommen...
Social recommendation has shown promising improvements over traditional
...
Cross-Domain Recommendation (CDR) has been popularly studied to utilize
...
Cross Domain Recommendation (CDR) has been popularly studied to alleviat...
In federated learning (FL) problems, client sampling plays a key role in...
Recently, generalization bounds of the non-convex empirical risk minimiz...
Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) ...
With the increasing demands for privacy protection, privacy-preserving
m...
To address the long-standing data sparsity problem in recommender system...
Deep Neural Networks (DNNs) have achieved remarkable progress in various...
Federated learning (FL) has attracted increasing attention in recent yea...
Knowledge Graph (KG) has attracted more and more companies' attention fo...
Graph Neural Networks (GNNs) have achieved remarkable performance by tak...
Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR)...
Recently, Graph Neural Network (GNN) has achieved remarkable progresses ...
Points of interest (POI) recommendation has been drawn much attention
re...
Recently latent factor model (LFM) has been drawing much attention in
re...
Point-of-Interest (POI) recommendation has been extensively studied and
...
We present, GEM, the first heterogeneous graph neural network approach f...
Secure online transaction is an essential task for e-commerce platforms....
Time-series forecasting is an important task in both academic and indust...
In this paper, we present a general multiparty model-ing paradigm with
P...
Nowadays, privacy preserving machine learning has been drawing much atte...
With online payment platforms being ubiquitous and important, fraud
tran...
With the growing emphasis on users' privacy, federated learning has beco...
Bayesian deep learning is recently regarded as an intrinsic way to
chara...
In this paper, we aim to understand the generalization properties of
gen...
Artificial intelligence (AI) is the core technology of technological
rev...
Internet companies are facing the need of handling large scale machine
l...