no code implementations • 1 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.
no code implementations • 29 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.
no code implementations • 25 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.
1 code implementation • 21 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.
1 code implementation • 7 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.
no code implementations • 4 Feb 2023 • Fuyuan Lyu, Xing Tang, Dugang Liu, Haolun Wu, Chen Ma, Xiuqiang He, Xue Liu
Representation learning has been a critical topic in machine learning.
1 code implementation • 26 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.
no code implementations • 28 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.
no code implementations • 29 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.
no code implementations • 6 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.
1 code implementation • Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021 • Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Shuai Li, Ruiming Tang, Xiuqiang He, Jianye Hao, Yong Yu
To better exploit search logs and model users' behavior patterns, numerous click models are proposed to extract users' implicit interaction feedback.
no code implementations • 12 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.
no code implementations • 24 Apr 2022 • Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang
Deep learning-based recommendation has become a widely adopted technique in various online applications.
no code implementations • 23 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.
no code implementations • 17 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.
no code implementations • 4 Feb 2022 • Verena Häberle, Ali Tayyebi, Xiuqiang He, Eduardo Prieto-Araujo, Florian Dörfler
We present a novel grid-forming control design approach for dynamic virtual power plants (DVPP).
no code implementations • 16 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.
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.
no code implementations • 3 Dec 2021 • Yankai Chen, Yifei Zhang, Yingxue Zhang, Huifeng Guo, Jingjie Li, Ruiming Tang, Xiuqiang He, Irwin King
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
no code implementations • 30 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).
no code implementations • 30 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.
no code implementations • 16 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.
1 code implementation • 5 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.
1 code implementation • Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 • Bo Chen, Yichao Wang, Zhirong Liu, Ruiming Tang, Wei Guo, Hongkun Zheng, Weiwei Yao, Muyu Zhang, Xiuqiang He
The state-of-the-art deep CTR models with parallel structure (e. g., DCN) learn explicit and implicit feature interactions through independent parallel networks.
1 code implementation • 28 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.
Ranked #4 on Recommendation Systems on Amazon-Book
no code implementations • 28 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.
no code implementations • 25 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.
no code implementations • 18 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.
2 code implementations • 26 Sep 2021 • Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He
Collaborative filtering (CF) is a widely studied research topic in recommender systems.
Ranked #3 on Recommendation Systems on Yelp2018
1 code implementation • 26 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.
no code implementations • 17 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.
no code implementations • 2 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.
no code implementations • 12 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.
1 code implementation • 11 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.
1 code implementation • 3 Aug 2021 • Fuyuan Lyu, Xing Tang, Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, Xue Liu
As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models.
Ranked #2 on Click-Through Rate Prediction on Avazu
no code implementations • 9 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.
no code implementations • 1 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.
no code implementations • 22 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.
no code implementations • 21 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.
1 code implementation • 17 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.
1 code implementation • 13 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.
no code implementations • 1 Apr 2021 • Dong Yao, Shengyu Zhang, Zhou Zhao, Wenyan Fan, Jieming Zhu, Xiuqiang He, Fei Wu
Personalized recommendation system has become pervasive in various video platform.
1 code implementation • 16 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.
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.
no code implementations • 20 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.
no code implementations • 8 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.
no code implementations • 1 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.
2 code implementations • 12 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.
no code implementations • 4 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.
no code implementations • 26 Aug 2020 • Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
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.
1 code implementation • 19 Aug 2020 • Zhu Zhang, Zhijie Lin, Zhou Zhao, Jieming Zhu, Xiuqiang He
Thus, these methods fail to distinguish the target moment from plausible negative moments.
no code implementations • 18 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.
no code implementations • 14 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.
4 code implementations • 25 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.
Ranked #17 on Click-Through Rate Prediction on Criteo
no code implementations • 22 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.
no code implementations • 1 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.
1 code implementation • 3 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.
no code implementations • 24 May 2019 • Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, Zhiwen Yu
In fact, user's behaviors from different domains regarding the same items are usually relevant.
7 code implementations • 1 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.
6 code implementations • 12 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.
no code implementations • 22 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.
19 code implementations • 13 Mar 2017 • Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Ranked #1 on Click-Through Rate Prediction on Company*