169 citations · 414 across the 24 of their papers we have counts for
11 papers · 1 filter
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Huiming Chen, Huandong Wang, Quanming Yao +3
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The prima…
Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction
Huandong Wang, Qiaohong Yu, Yu Liu +2
With the rapid development of the mobile communication technology, mobile trajectories of humans are massively collected by Internet service providers (ISPs) and application servic…
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
Zirui Zhu, Chen Gao, Xu Chen +3
Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities. While many promising resu…
Improving Location Recommendation with Urban Knowledge Graph
Chang Liu, Chen Gao, Depeng Jin +1
Location recommendation is defined as to recommend locations (POIs) to users in location-based services. The existing data-driving approaches of location recommendation suffer from…
Knowledge-driven Site Selection via Urban Knowledge Graph
Yu Liu, Jingtao Ding, Yong Li
Site selection determines optimal locations for new stores, which is of crucial importance to business success. Especially, the wide application of artificial intelligence with mul…
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Xiawei Guo, Yuhan Quan, Huan Zhao +3
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing wo…
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
Zefang Zong, Jingwei Wang, Tao Feng +3
Recent technology development brings the boom of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehous…
Efficient Data-specific Model Search for Collaborative Filtering
Chen Gao, Quanming Yao, Depeng Jin +1
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences…
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Fuxian Li, Jie Feng, Huan Yan +3
Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent tra…
Role-Aware Modeling for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play diffe…
Learnable Embedding Sizes for Recommender Systems
Siyi Liu, Chen Gao, Yihong Chen +2
The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manne…