1 code implementation • 24 May 2024 • Bowei He, Yunpeng Weng, Xing Tang, Ziqiang Cui, Zexu Sun, Liang Chen, Xiuqiang He, Chen Ma
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts.
no code implementations • 2 Apr 2024 • Xin Zhang, Ling Chen, Xing Tang, Hongyu Shi
To this end, we propose a Dual-view Supergrid-aware Graph Neural Network (DSGNN) for regional air quality estimation, which can model the spatial dependencies of distant grid regions from dual views (i. e., satellite-derived aerosol optical depth (AOD) and meteorology).
no code implementations • 26 Mar 2024 • Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu, Xiuqiang He
In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization.
1 code implementation • 28 Jan 2024 • Dan Zhang, Yangliao Geng, Wenwen Gong, Zhongang Qi, Zhiyu Chen, Xing Tang, Ying Shan, Yuxiao Dong, Jie Tang
In this work, we investigate how to employ both batch-wise CL (BCL) and feature-wise CL (FCL) for recommendation.
1 code implementation • 12 Jan 2024 • Ziqiang Cui, Xing Tang, Yang Qiao, Bowei He, Liang Chen, Xiuqiang He, Chen Ma
Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.
no code implementations • 3 Jan 2024 • Yunpeng Weng, Xing Tang, Liang Chen, Dugang Liu, Xiuqiang He
In addition to predicting the click-through rate (CTR) or the conversion rate (CVR) as in traditional recommendations, it is essential for FinTech platforms to estimate the customers' purchase amount for each delivered fund and achieve an effective allocation of impressions based on the predicted results to optimize the total expected transaction value (ETV).
no code implementations • 6 Nov 2023 • Fuyuan Lyu, Yaochen Hu, Xing Tang, Yingxue Zhang, Ruiming Tang, Xue Liu
Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.
1 code implementation • NeurIPS 2023 • Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Weihong Luo, Liang Chen, Xiuqiang He, Xue Liu
In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.
no code implementations • 29 Aug 2023 • Hong Zhu, Runpeng Yu, Xing Tang, Yifei Wang, Yuan Fang, Yisen Wang
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
no code implementations • 23 Jun 2023 • Xing Tang, Yang Qiao, Yuwen Fu, Fuyuan Lyu, Dugang Liu, Xiuqiang He
Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations.
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 • 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 • 22 Feb 2023 • Xing Tang, Ling Chen
GTRL is the first work that incorporates the entity group modeling to capture the correlation between entities by stacking only a finite number of layers.
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.
Ranked #3 on Click-Through Rate Prediction on KDD12
no code implementations • 12 Dec 2022 • Shiwei Li, Huifeng Guo, Lu Hou, Wei zhang, Xing Tang, Ruiming Tang, Rui Zhang, Ruixuan Li
To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT).
1 code implementation • 25 Nov 2022 • Rong Hu, Ling Chen, Shenghuan Miao, Xing Tang
SWL-Adapt calculates sample weights according to the classification loss and domain discrimination loss of each sample with a parameterized network.
1 code implementation • 9 Aug 2022 • Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu
To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.
Ranked #2 on Click-Through Rate Prediction on KDD12
1 code implementation • 11 Jul 2022 • Ben Xue, Shenghui Ran, Quan Chen, Rongfei Jia, Binqiang Zhao, Xing Tang
Image color harmonization algorithm aims to automatically match the color distribution of foreground and background images captured in different conditions.
Ranked #9 on Image Harmonization on iHarmony4
1 code implementation • 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.
no code implementations • CVPR 2022 • Jian Zhang, Yuanqing Zhang, Huan Fu, Xiaowei Zhou, Bowen Cai, Jinchi Huang, Rongfei Jia, Binqiang Zhao, Xing Tang
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
1 code implementation • 1 Nov 2021 • Ling Chen, Jun Cui, Xing Tang, Chaodu Song, Yuntao Qian, Yansheng Li, Yongjun Zhang
Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings.
1 code implementation • 26 Oct 2021 • Ling Chen, Da Wang, Dandan Lyu, Xing Tang, Hongyu Shi
Evolving temporal networks serve as the abstractions of many real-life dynamic systems, e. g., social network and e-commerce.
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 #1 on Click-Through Rate Prediction on Avazu
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.