31 citations · 69 across the 16 of their papers we have counts for
16 papers
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…
Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms
Yunpeng Weng, Xing Tang, Liang Chen +2
FinTech platforms facilitated by digital payments are watching growth rapidly, which enable the distribution of mutual funds personalized to individual investors via mobile Apps. A…
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…
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction
Xing Tang, Yang Qiao, Yuwen Fu +3
A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them si…
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,…
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing
Yunpeng Weng, Xing Tang, Liang Chen +1
Multi-task learning for various real-world applications usually involves tasks with logical sequential dependence. For example, in online marketing, the cascade behavior pattern of…
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Dugang Liu, Yang Qiao, Xing Tang +4
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most o…
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Fuyuan Lyu, Xing Tang, Dugang Liu +4
Representation learning has been a critical topic in machine learning. In Click-through Rate Prediction, most features are represented as embedding vectors and learned simultaneous…
Optimizing Feature Set for Click-Through Rate Prediction
Fuyuan Lyu, Xing Tang, Dugang Liu +3
Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Ther…
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…
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…
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…
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…
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…