542 citations · 1.5k across the 65 of their papers we have counts for
65 papers
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…
Nonlinear Stability of Complex Droop Control in Converter-Based Power Systems
Xiuqiang He, Verena Häberle, Irina Subotić +1
In this letter, we study the nonlinear stability problem of converter-based power systems, where the converter dynamics are governed by a complex droop control. This complex droop…
Complex-Frequency Synchronization of Converter-Based Power Systems
Xiuqiang He, Verena Häberle, Florian Dörfler
In this paper, we study phase-amplitude multivariable dynamics in converter-based power systems from a complex-frequency perspective. Complex frequency represents the rate of chang…
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…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…
A Graph-Enhanced Click Model for Web Search
Jianghao Lin, Weiwen Liu, Xinyi Dai +6
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback. Most traditional click models…
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Guohao Cai, Jieming Zhu, Quanyu Dai +4
Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users…
Contrastive Learning with Positive-Negative Frame Mask for Music Representation
Dong Yao, Zhou Zhao, Shengyu Zhang +4
Self-supervised learning, especially contrastive learning, has made an outstanding contribution to the development of many deep learning research fields. Recently, researchers in t…
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Yi Li, Jieming Zhu, Weiwen Liu +6
The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has receive…
Grid-Forming and Spatially Distributed Control Design of Dynamic Virtual Power Plants
Verena Häberle, Ali Tayyebi, Xiuqiang He +2
We present a novel grid-forming control design approach for dynamic virtual power plants (DVPP). We consider a group of heterogeneous grid-forming distributed energy resources (DER…
Debiased Recommendation with User Feature Balancing
Mengyue Yang, Guohao Cai, Furui Liu +5
Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS…
AIM: Automatic Interaction Machine for Click-Through Rate Prediction
Chenxu Zhu, Bo Chen, Weinan Zhang +5
Feature embedding learning and feature interaction modeling are two crucial components of deep models for Click-Through Rate (CTR) prediction. Most existing deep CTR models suffer…
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
Yankai Chen, Yifei Zhang, Yingxue Zhang +5
Due to the promising advantages in space compression and inference acceleration, quantized representation learning for recommender systems has become an emerging research direction…
QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback
Handong Ma, Jiawei Hou, Chenxu Zhu +6
Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search…
Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation
Changjun He, Xiuqiang He, Hua Geng +2
This study examines the transient stability of low-inertia power systems with inverter-based generation (IBG) and proposes a sufficient stability criterion. In low-inertia grids, t…
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
Wei Guo, Can Zhang, Zhicheng He +6
CTR prediction is essential for modern recommender systems. Ranging from early factorization machines to deep learning based models in recent years, existing CTR methods focus on c…
Cross-Batch Negative Sampling for Training Two-Tower Recommenders
Jinpeng Wang, Jieming Zhu, Xiuqiang He
The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems. Many two-tower models are tra…