Search Results for author: Xiuqiang He

Found 63 papers, 23 papers with code

Explicit Feature Interaction-aware Uplift Network for Online Marketing

no code implementations1 Jun 2023 Dugang Liu, Xing Tang, Han Gao, Fuyuan Lyu, Xiuqiang He

Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i. e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario.


MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches

no code implementations29 Apr 2023 Verena Häberle, Linbin Huang, Xiuqiang He, Roy S. Smith, Florian Dörfler

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 converters.

Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing

no code implementations25 Apr 2023 Yunpeng Weng, Xing Tang, Liang Chen, Xiuqiang He

For example, in online marketing, the cascade behavior pattern of $impression \rightarrow click \rightarrow conversion$ is usually modeled as multiple tasks in a multi-task manner, where the sequential dependence between tasks is simply connected with an explicitly defined function or implicitly transferred information in current works.

Marketing Multi-Task Learning

Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset

1 code implementation21 Mar 2023 Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan, Zhong Ming

To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.

Recommendation Systems

Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation

1 code implementation7 Feb 2023 Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Weike Pan, Zhong Ming

Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.


Optimizing Feature Set for Click-Through Rate Prediction

1 code implementation26 Jan 2023 Fuyuan Lyu, Xing Tang, Dugang Liu, Liang Chen, Xiuqiang He, Xue Liu

Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates.

Click-Through Rate Prediction

Nonlinear Stability of Complex Droop Control in Converter-Based Power Systems

no code implementations28 Oct 2022 Xiuqiang He, Verena Häberle, Irina Subotić, Florian Dörfler

In previous work, the global stability of dVOC (i. e., complex droop control) has been proven by prespecifying a nominal synchronous steady state.

Complex-Frequency Synchronization of Converter-Based Power Systems

no code implementations29 Aug 2022 Xiuqiang He, Verena Häberle, Florian Dörfler

We propose the notion of complex-frequency synchronization to study the phase-amplitude coupled stability of a power system with grid-forming virtual oscillator-controlled converters.

DIWIFT: Discovering Instance-wise Influential Features for Tabular Data

no code implementations6 Jul 2022 Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He

Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.

feature selection

Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

no code implementations12 Jun 2022 Runpeng Yu, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang, Nanyang Ye, Shao-Lun Huang, Xiuqiang He

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.


PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

no code implementations23 Mar 2022 Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He

Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.

Recommendation Systems Re-Ranking

Contrastive Learning with Positive-Negative Frame Mask for Music Representation

no code implementations17 Mar 2022 Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, Xiuqiang He

We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.

Contrastive Learning Cover song identification +2

Debiased Recommendation with User Feature Balancing

no code implementations16 Jan 2022 Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen

To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.

Causal Inference Recommendation Systems

Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks

1 code implementation CVPR 2022 Wenwen Pan, Haonan Shi, Zhou Zhao, Jieming Zhu, Xiuqiang He, Zhigeng Pan, Lianli Gao, Jun Yu, Fei Wu, Qi Tian

Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions.

Denoising Semantic Segmentation +2

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

no code implementations30 Nov 2021 Wei Guo, Can Zhang, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang

With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).

Click-Through Rate Prediction Contrastive Learning +3

Transient Stability of Low-Inertia Power Systems with Inverter-Based Generation

no code implementations30 Nov 2021 Changjun He, Xiuqiang He, Hua Geng, Huadong Sun, Shiyun Xu

This criterion is proved to be a sufficient stability condition for addressing the effects of the jumps and cosine damping coefficient on the system stability.

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

no code implementations16 Nov 2021 Handong Ma, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu

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 results.

Question Answering Semantic Similarity +1

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

1 code implementation5 Nov 2021 Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.

Click-Through Rate Prediction

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

1 code implementation28 Oct 2021 Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He

With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains.

Collaborative Filtering Recommendation Systems

Cross-Batch Negative Sampling for Training Two-Tower Recommenders

no code implementations28 Oct 2021 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.

Recommendation Systems Vocal Bursts Valence Prediction

Content Filtering Enriched GNN Framework for News Recommendation

no code implementations25 Oct 2021 Yong Gao, Huifeng Guo, Dandan Lin, Yingxue Zhang, Ruiming Tang, Xiuqiang He

It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously.

Collaborative Filtering News Recommendation

Context-aware Reranking with Utility Maximization for Recommendation

no code implementations18 Oct 2021 Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu

As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.

Graph Attention Recommendation Systems

Why Do We Click: Visual Impression-aware News Recommendation

1 code implementation26 Sep 2021 Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu

In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.

Decision Making News Recommendation

Mode Clustering Based Dynamic Equivalent Modeling of Wind Farm for Small-Signal Stability Analysis

no code implementations17 Sep 2021 Xiuqiang He, Hua Geng, Geng Yang

It is deemed that a DEM can be used to represent the whole WF to evaluate its impact on the SSS of power systems, as long as the frequency response of the DEM adequately matches that of the detailed WF model around the frequency of oscillation modes of concern.

Top-N Recommendation with Counterfactual User Preference Simulation

no code implementations2 Sep 2021 Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang

To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.

Causal Inference Recommendation Systems

Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm

no code implementations12 Aug 2021 Lin Bo, Liang Pang, Gang Wang, Jun Xu, Xiuqiang He, Ji-Rong Wen

Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.

Document Ranking Information Retrieval +3

Retrieval & Interaction Machine for Tabular Data Prediction

1 code implementation11 Aug 2021 Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.

Click-Through Rate Prediction Recommendation Systems +1

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

no code implementations9 Jun 2021 Xiangli Yang, Qing Liu, Rong Su, Ruiming Tang, Zhirong Liu, Xiuqiang He

The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.

Click-Through Rate Prediction Recommendation Systems +1

Dual Graph enhanced Embedding Neural Network for CTR Prediction

no code implementations1 Jun 2021 Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He

To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.

Click-Through Rate Prediction Recommendation Systems

Transient Stability of Hybrid Power Systems Dominated by Different Types of Grid-Forming Devices

no code implementations22 Apr 2021 Xiuqiang He, Hua Geng

This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices.

Deep Learning for Click-Through Rate Estimation

no code implementations21 Apr 2021 Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, Xiuqiang He

In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks.

Recommendation Systems

ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table

1 code implementation17 Apr 2021 Huifeng Guo, Wei Guo, Yong Gao, Ruiming Tang, Xiuqiang He, Wenzhi Liu

Different from the models with dense training data, the training data for CTR models is usually high-dimensional and sparse.

An Adversarial Imitation Click Model for Information Retrieval

1 code implementation13 Apr 2021 Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.

Imitation Learning Information Retrieval +2

An Embedding Learning Framework for Numerical Features in CTR Prediction

1 code implementation16 Dec 2020 Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He

In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved.

Click-Through Rate Prediction Feature Engineering +1

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding

no code implementations NeurIPS 2020 Zhu Zhang, Zhou Zhao, Zhijie Lin, Jieming Zhu, Xiuqiang He

Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.

Contrastive Learning

Synchronization Instability of Inverter-Based Generation During Asymmetrical Grid Faults

no code implementations20 Nov 2020 Xiuqiang He, Changjun He, Sisi Pan, Hua Geng, Feng Liu

In contrast, both positive- and negative-sequence synchronizations should be of concern for inverter-based generation (IBG) under asymmetrical faults.

Ensembled CTR Prediction via Knowledge Distillation

no code implementations8 Nov 2020 Jieming Zhu, Jinyang Liu, Weiqi Li, Jincai Lai, Xiuqiang He, Liang Chen, Zibin Zheng

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.

Click-Through Rate Prediction Knowledge Distillation

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +1

BarsCTR: Open Benchmarking for Click-Through Rate Prediction

2 code implementations12 Sep 2020 Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He

We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.

Benchmarking Click-Through Rate Prediction +1

A Practical Incremental Method to Train Deep CTR Models

no code implementations4 Sep 2020 Yichao Wang, Huifeng Guo, Ruiming Tang, Zhirong Liu, Xiuqiang He

Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data.

Incremental Learning Recommendation Systems

A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks

1 code implementation Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates

Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.

Recommendation Systems

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

no code implementations18 Jun 2020 Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.

Decision Making Recommendation Systems +3

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

no code implementations14 Apr 2020 Yichao Wang, Xiangyu Zhang, Zhirong Liu, Zhenhua Dong, Xinhua Feng, Ruiming Tang, Xiuqiang He

To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user.

Recommendation Systems Re-Ranking

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

4 code implementations25 Mar 2020 Bin Liu, Chenxu Zhu, Guilin Li, Wei-Nan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.

Click-Through Rate Prediction Recommendation Systems

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

no code implementations22 Jan 2020 Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.

Meta-Learning Model Selection +1

Multi-Graph Convolution Collaborative Filtering

no code implementations1 Jan 2020 Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He

In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.

Collaborative Filtering

Less Is Better: Unweighted Data Subsampling via Influence Function

1 code implementation3 Dec 2019 Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang

In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.

General Classification Image Classification +2

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

7 code implementations1 Jul 2018 Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

Click-Through Rate Prediction Feature Engineering +3

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

6 code implementations12 Apr 2018 Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong

In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.

Click-Through Rate Prediction Feature Engineering +1

Federated Meta-Learning with Fast Convergence and Efficient Communication

no code implementations22 Feb 2018 Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He

Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.

Federated Learning Meta-Learning +1

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