31 citations · 69 across the 16 of their papers we have counts for
8 papers · 1 filter
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
Ziqiang Cui, Xing Tang, Yang Qiao +4
Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden co…
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
Fuyuan Lyu, Xing Tang, Dugang Liu +5
Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection i…
Explicit Feature Interaction-aware Uplift Network for Online Marketing
Dugang Liu, Xing Tang, Han Gao +2
As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts,…
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
Shiwei Li, Huifeng Guo, Lu Hou +5
Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their…
DIWIFT: Discovering Instance-wise Influential Features for Tabular Data
Dugang Liu, Pengxiang Cheng, Hong Zhu +6
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce. The success of these web applications l…
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…
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
Fuyuan Lyu, Xing Tang, Huifeng Guo +4
Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and ite…
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…