9 citations · 24 across the 10 of their papers we have counts for
10 papers
SCaLRec: Semantic Calibration for LLM-enabled Cloud-Device Sequential Recommendation
Ruiqi Zheng, Jinli Cao, Jiao Yin +1
Cloud-device collaborative recommendation partitions computation across the cloud and user devices: the cloud provides semantic user modeling, while the device leverages recent int…
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…
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…
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Liang Qu, Wei Yuan, Ruiqi Zheng +3
Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating…
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…