31 citations · 45 across the 4 of their papers we have counts for
4 papers
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
Fuyuan Lyu, Xing Tang, Hong Zhu +4
Learning embedding table plays a fundamental role in Click-through rate(CTR) prediction from the view of the model performance and memory usage. The embedding table is a two-dimens…
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining
Ling Chen, Jun Cui, Xing Tang +4
Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the…
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation
Ling Chen, Da Wang, Dandan Lyu +2
Evolving temporal networks serve as the abstractions of many real-life dynamic systems, e.g., social network and e-commerce. The purpose of temporal network embedding is to map eac…
Learning Cross-Domain Representation with Multi-Graph Neural Network
Yi Ouyang, Bin Guo, Xing Tang +3
Learning effective embedding has been proved to be useful in many real-world problems, such as recommender systems, search ranking and online advertisement. However, one of the cha…