79 citations · 128 across the 11 of their papers we have counts for
11 papers
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
Ziyang Liu, Chaokun Wang, Jingcao Xu +5
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growi…
Personalized Elastic Embedding Learning for On-Device Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact rec…
Instant Representation Learning for Recommendation over Large Dynamic Graphs
Cheng Wu, Chaokun Wang, Jingcao Xu +7
Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendat…
Multi-behavior Self-supervised Learning for Recommendation
Jingcao Xu, Chaokun Wang, Cheng Wu +6
Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understa…
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
Jing Long, Tong Chen, Nguyen Quoc Viet Hung +3
As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and inte…
Continuous Input Embedding Size Search For Recommender Systems
Yunke Qu, Tong Chen, Xiangyu Zhao +3
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-val…
ReFRS: Resource-efficient Federated Recommender System for Dynamic and Diversified User Preferences
Mubashir Imran, Hongzhi Yin, Tong Chen +3
Owing to its nature of scalability and privacy by design, federated learning (FL) has received increasing interest in decentralized deep learning. FL has also facilitated recent re…
Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever
Jin Chen, Defu Lian, Yucheng Li +3
Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the…
Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
Jin Chen, Defu Lian, Binbin Jin +3
Variational AutoEncoder (VAE) has been extended as a representative nonlinear method for collaborative filtering. However, the bottleneck of VAE lies in the softmax computation ove…
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Jin Chen, Tiezheng Ge, Gangwei Jiang +3
Ad creatives are one of the prominent mediums for online e-commerce advertisements. Ad creatives with enjoyable visual appearance may increase the click-through rate (CTR) of produ…
Automated Creative Optimization for E-Commerce Advertising
Jin Chen, Ju Xu, Gangwei Jiang +4
Advertising creatives are ubiquitous in E-commerce advertisements and aesthetic creatives may improve the click-through rate (CTR) of the products. Nowadays smart advertisement pla…