6 citations · 12 across the 32 of their papers we have counts for
10 papers · 1 filter
Sparser Training for On-Device Recommendation Systems
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems often rely on large embedding tables that map users and items to dense vectors of uniform size, leading to substantial memory consumption and inefficiencies. Th…
Harnessing Large Language Models for Group POI Recommendations
Jing Long, Liang Qu, Junliang Yu +3
The rapid proliferation of Location-Based Social Networks (LBSNs) has underscored the importance of Point-of-Interest (POI) recommendation systems in enhancing user experiences. Wh…
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
Hung Vinh Tran, Tong Chen, Guanhua Ye +3
Content-based Recommender Systems (CRSs) play a crucial role in shaping user experiences in e-commerce, online advertising, and personalized recommendations. However, due to the va…
DecKG: Decentralized Collaborative Learning with Knowledge Graph Enhancement for POI Recommendation
Ruiqi Zheng, Liang Qu, Guanhua Ye +3
Decentralized collaborative learning for Point-of-Interest (POI) recommendation has gained research interest due to its advantages in privacy preservation and efficiency, as it kee…
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can…
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Yunke Qu, Liang Qu, Tong Chen +3
Recommender systems typically represent users and items by learning their embeddings, which are usually set to uniform dimensions and dominate the model parameters. However, real-w…
Poisoning Decentralized Collaborative Recommender System and Its Countermeasures
Ruiqi Zheng, Liang Qu, Tong Chen +3
To make room for privacy and efficiency, the deployment of many recommender systems is experiencing a shift from central servers to personal devices, where the federated recommende…
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
Jing Long, Tong Chen, Guanhua Ye +3
As an indispensable personalized service within Location-Based Social Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist individuals in discovering attract…
Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
In Location-based Social Networks, Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the cloud-based model to on-device…
On-Device Recommender Systems: A Comprehensive Survey
Hongzhi Yin, Liang Qu, Tong Chen +6
Recommender systems have been widely deployed in various real-world applications to help users identify content of interest from massive amounts of information. Traditional recomme…