542 citations · 1.5k across the 65 of their papers we have counts for
8 papers · 1 filter
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,…
MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches
Verena Häberle, Linbin Huang, Xiuqiang He +3
A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected conver…
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…
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
Dugang Liu, Pengxiang Cheng, Zinan Lin +6
Debiased recommendation with a randomized dataset has shown very promising results in mitigating the system-induced biases. However, it still lacks more theoretical insights or an…
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…