6 citations · 16 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
Automated Similarity Metric Generation for Recommendation
Liang Qu, Yun Lin, Wei Yuan +3
The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined sim…
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…
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…
Single-shot Embedding Dimension Search in Recommender System
Liang Qu, Yonghong Ye, Ningzhi Tang +3
As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, th…
AutoML for Deep Recommender Systems: A Survey
Ruiqi Zheng, Liang Qu, Bin Cui +2
Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep…