Search Results for author: Zhenhua Dong

Found 37 papers, 15 papers with code

Semantic Sentence Matching via Interacting Syntax Graphs

no code implementations COLING 2022 Chen Xu, Jun Xu, Zhenhua Dong, Ji-Rong Wen

In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures.

Graph Matching Sentence

Optimal Partial Transport Based Sentence Selection for Long-form Document Matching

1 code implementation COLING 2022 Weijie Yu, Liang Pang, Jun Xu, Bing Su, Zhenhua Dong, Ji-Rong Wen

Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results.


Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation

no code implementations30 Nov 2023 Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang

Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.


Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

1 code implementation16 Aug 2023 Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, Ji-Rong Wen

In the video recommendation, watch time is commonly adopted as an indicator of user interest.

DisCover: Disentangled Music Representation Learning for Cover Song Identification

no code implementations19 Jul 2023 Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu, Liqun Deng, Zhou Zhao, Zhenhua Dong, RuiQi Li, Lichao Zhang, Fei Wu

We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning.

Blocking Cover song identification +3

ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

1 code implementation15 Jun 2023 Jieming Zhu, Guohao Cai, JunJie Huang, Zhenhua Dong, Ruiming Tang, Weinan Zhang

The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.

Recommendation Systems

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

4 code implementations3 Apr 2023 Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong

As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.

Click-Through Rate Prediction feature selection +1

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

1 code implementation2 Apr 2023 Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, Xiao-Ming Wu

Item list continuation is proposed to model the overall trend of a list and predict subsequent items.

Fair-CDA: Continuous and Directional Augmentation for Group Fairness

no code implementations1 Apr 2023 Rui Sun, Fengwei Zhou, Zhenhua Dong, Chuanlong Xie, Lanqing Hong, Jiawei Li, Rui Zhang, Zhen Li, Zhenguo Li

By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.

Data Augmentation Disentanglement +1

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

A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction

no code implementations12 Nov 2022 Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Jie Sun

However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.

Generalization Bounds Imputation +1

Recommendation with User Active Disclosing Willingness

no code implementations25 Oct 2022 Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong

Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production. Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation.

Recommendation Systems

Law Article-Enhanced Legal Case Matching: a Causal Learning Approach

1 code implementation20 Oct 2022 Zhongxiang Sun, Jun Xu, Xiao Zhang, Zhenhua Dong, Ji-Rong Wen

We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.

Semantic Text Matching Text Matching

IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System

2 code implementations18 Oct 2022 Xiangyang Li, Bo Chen, Huifeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, JinXing Liu, Zhenhua Dong, Ruiming Tang

FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.

A Brief History of Recommender Systems

no code implementations5 Sep 2022 Zhenhua Dong, Zhe Wang, Jun Xu, Ruiming Tang, JiRong Wen

Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry.

Recommendation Systems

Debiased Recommendation with Neural Stratification

no code implementations15 Aug 2022 Quanyu Dai, Zhenhua Dong, Xu Chen

Debiased recommender models have recently attracted increasing attention from the academic and industry communities.

Multiple Robust Learning for Recommendation

no code implementations9 Jul 2022 Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu

Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.

Imputation Recommendation Systems

How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis

no code implementations Findings (ACL) 2022 Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu

We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

1 code implementation23 Feb 2022 Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou

To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.

Causal Inference Descriptive +2

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

no code implementations18 Jan 2022 Peng Wu, Haoxuan Li, yuhao deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.

Causal Inference Recommendation Systems

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

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

1 code implementation26 Sep 2021 Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He

While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.

Collaborative Filtering Recommendation Systems

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 counterfactual +1

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

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

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 +3

Uncovering Download Fraud Activities in Mobile App Markets

no code implementations5 Jul 2019 Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu

To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.

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

8 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 +2

Federated Meta-Learning with Fast Convergence and Efficient Communication

1 code implementation22 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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