Search Results for author: Fei Sun

Found 67 papers, 26 papers with code

Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation

no code implementations Findings (ACL) 2022 ZeFeng Cai, LinLin Wang, Gerard de Melo, Fei Sun, Liang He

Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation.

Explanation Generation Recommendation Systems

TEA: Test-time Energy Adaptation

no code implementations24 Nov 2023 Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

To address this, we propose a novel energy-based perspective, enhancing the model's perception of target data distributions without requiring access to training data or processes.


Robust Recommender System: A Survey and Future Directions

no code implementations5 Sep 2023 Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, HuaWei Shen, Xueqi Cheng

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.

Fairness Recommendation Systems +1

A Large Language Model Enhanced Conversational Recommender System

no code implementations11 Aug 2023 Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun

For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.

Language Modelling Large Language Model +2

PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

no code implementations25 May 2023 Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.

Data Augmentation

Rethinking GNN-based Entity Alignment on Heterogeneous Knowledge Graphs: New Datasets and A New Method

no code implementations7 Apr 2023 Xuhui Jiang, Chengjin Xu, Yinghan Shen, Fenglong Su, Yuanzhuo Wang, Fei Sun, Zixuan Li, HuaWei Shen

In this paper, to study the performance of EA methods in realistic settings, we focus on the alignment of highly heterogeneous KGs (HHKGs) (e. g., event KGs and general KGs) which are different with regard to the scale and structure, and share fewer overlapping entities.

Entity Alignment Knowledge Graphs

LegoNet: A Fast and Exact Unlearning Architecture

no code implementations28 Oct 2022 Sihao Yu, Fei Sun, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng

However, such a strategy typically leads to a loss in model performance, which poses the challenge that increasing the unlearning efficiency while maintaining acceptable performance.

Representation Learning

MILAN: Masked Image Pretraining on Language Assisted Representation

1 code implementation11 Aug 2022 Zejiang Hou, Fei Sun, Yen-Kuang Chen, Yuan Xie, Sun-Yuan Kung

When the masked autoencoder is pretrained and finetuned on ImageNet-1K dataset with an input resolution of 224x224, MILAN achieves a top-1 accuracy of 85. 4% on ViT-Base, surpassing previous state-of-the-arts by 1%.

Semantic Segmentation

Adversarial Camouflage for Node Injection Attack on Graphs

1 code implementation3 Aug 2022 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Fei Sun, Xueqi Cheng

In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.

Debiasing Learning for Membership Inference Attacks Against Recommender Systems

1 code implementation24 Jun 2022 Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren

To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.

Recommendation Systems

Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation

no code implementations1 Apr 2022 Ziqian Chen, Fei Sun, Yifan Tang, Haokun Chen, Jinyang Gao, Bolin Ding

Then we study users' privacy decision making under different data disclosure mechanisms and recommendation models, and how their data disclosure decisions affect the recommender system's performance.

Decision Making Federated Learning +2

CHEX: CHannel EXploration for CNN Model Compression

1 code implementation CVPR 2022 Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung

However, conventional pruning methods have limitations in that: they are restricted to pruning process only, and they require a fully pre-trained large model.

Image Classification Instance Segmentation +4

Neural Re-ranking in Multi-stage Recommender Systems: A Review

1 code implementation14 Feb 2022 Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.

Recommendation Systems Re-Ranking

Recommendation Unlearning

1 code implementation18 Jan 2022 Chong Chen, Fei Sun, Min Zhang, Bolin Ding

From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget these data to regain utility.

Recommendation Systems

Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting

no code implementations21 Dec 2021 Minghai Qin, Tianyun Zhang, Fei Sun, Yen-Kuang Chen, Makan Fardad, Yanzhi Wang, Yuan Xie

Deep neural networks (DNNs) have shown to provide superb performance in many real life applications, but their large computation cost and storage requirement have prevented them from being deployed to many edge and internet-of-things (IoT) devices.

Graph Attention

Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks

no code implementations20 Dec 2021 Fei Sun, Minghai Qin, Tianyun Zhang, Xiaolong Ma, Haoran Li, Junwen Luo, Zihao Zhao, Yen-Kuang Chen, Yuan Xie

Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns.

Machine Translation speech-recognition +1

Counterfactual Evaluation for Explainable AI

no code implementations5 Sep 2021 Yingqiang Ge, Shuchang Liu, Zelong Li, Shuyuan Xu, Shijie Geng, Yunqi Li, Juntao Tan, Fei Sun, Yongfeng Zhang

While recent years have witnessed the emergence of various explainable methods in machine learning, to what degree the explanations really represent the reasoning process behind the model prediction -- namely, the faithfulness of explanation -- is still an open problem.

Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation

1 code implementation Findings (EMNLP) 2021 Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice.

Abstractive Text Summarization

Effective Model Sparsification by Scheduled Grow-and-Prune Methods

1 code implementation ICLR 2022 Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie

It addresses the shortcomings of the previous works by repeatedly growing a subset of layers to dense and then pruning them back to sparse after some training.

Image Classification

CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation

no code implementations28 May 2021 Xu Xie, Zhaoyang Liu, Shiwen Wu, Fei Sun, Cihang Liu, Jiawei Chen, Jinyang Gao, Bin Cui, Bolin Ding

It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations.

Collaborative Filtering Recommendation Systems

Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

no code implementations20 May 2021 Yang Deng, Yaliang Li, Fei Sun, Bolin Ding, Wai Lam

However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure.

Decision Making Recommendation Systems +2

GRN: Generative Rerank Network for Context-wise Recommendation

no code implementations2 Apr 2021 Yufei Feng, Binbin Hu, Yu Gong, Fei Sun, Qingwen Liu, Wenwu Ou

Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely.

Recommendation Systems

Semantic Models for the First-stage Retrieval: A Comprehensive Review

1 code implementation8 Mar 2021 Jiafeng Guo, Yinqiong Cai, Yixing Fan, Fei Sun, Ruqing Zhang, Xueqi Cheng

We believe it is the right time to survey current status, learn from existing methods, and gain some insights for future development.

Re-Ranking Retrieval +1

Explore User Neighborhood for Real-time E-commerce Recommendation

no code implementations28 Feb 2021 Xu Xie, Fei Sun, Xiaoyong Yang, Zhao Yang, Jinyang Gao, Wenwu Ou, Bin Cui

On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information.

Collaborative Filtering Recommendation Systems

Variation Control and Evaluation for Generative SlateRecommendations

no code implementations26 Feb 2021 Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, Yongfeng Zhang

Slate recommendation generates a list of items as a whole instead of ranking each item individually, so as to better model the intra-list positional biases and item relations.

Recommendation Systems

Revisit Recommender System in the Permutation Prospective

no code implementations24 Feb 2021 Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, Wenwu Ou

Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.

Recommendation Systems Re-Ranking

Graph Attention Collaborative Similarity Embedding for Recommender System

no code implementations5 Feb 2021 Jinbo Song, Chao Chang, Fei Sun, Zhenyang Chen, Guoyong Hu, Peng Jiang

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning.

Collaborative Filtering Graph Attention +2

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Graph Neural Networks in Recommender Systems: A Survey

1 code implementation4 Nov 2020 Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload.

Graph Representation Learning Recommendation Systems

Contrastive Learning for Sequential Recommendation

1 code implementation27 Oct 2020 Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions.

Contrastive Learning Data Augmentation +1

XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

1 code implementation24 Oct 2020 Fuyu Lv, Mengxue Li, Tonglei Guo, Changlong Yu, Fei Sun, Taiwei Jin, Wilfred Ng

The offline experimental results based on real-world E-commerce data demonstrate the effectiveness and verify the importance of unclicked items in sequential recommendation.

Metric Learning Retrieval +1

NGAT4Rec: Neighbor-Aware Graph Attention Network For Recommendation

no code implementations23 Oct 2020 Jinbo Song, Chao Chang, Fei Sun, Xinbo Song, Peng Jiang

To modeling the implicit correlations of neighbors in graph embedding aggregating, we propose a Neighbor-Aware Graph Attention Network for recommendation task, termed NGAT4Rec.

Collaborative Filtering Graph Attention +2

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

no code implementations13 Aug 2020 Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou

In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.

Click-Through Rate Prediction Recommendation Systems

Ranking Enhanced Dialogue Generation

no code implementations13 Aug 2020 Changying Hao, Liang Pang, Yanyan Lan, Fei Sun, Jiafeng Guo, Xue-Qi Cheng

To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper.

Dialogue Generation Response Generation

Understanding Echo Chambers in E-commerce Recommender Systems

1 code implementation6 Jul 2020 Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang

Current research on recommender systems mostly focuses on matching users with proper items based on user interests.

Recommendation Systems

Learning Personalized Risk Preferences for Recommendation

1 code implementation6 Jul 2020 Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang

Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions.

Recommendation Systems

Computation on Sparse Neural Networks: an Inspiration for Future Hardware

no code implementations24 Apr 2020 Fei Sun, Minghai Qin, Tianyun Zhang, Liu Liu, Yen-Kuang Chen, Yuan Xie

We show that for practically complicated problems, it is more beneficial to search large and sparse models in the weight dominated region.

A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods

no code implementations12 Apr 2020 Tianyun Zhang, Xiaolong Ma, Zheng Zhan, Shanglin Zhou, Minghai Qin, Fei Sun, Yen-Kuang Chen, Caiwen Ding, Makan Fardad, Yanzhi Wang

To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories, i. e., static regularization-based pruning and dynamic regularization-based pruning.

Complex risk statistics with scenario analysis

no code implementations19 Mar 2020 Fei Sun, Yichuan Dong

Complex risk is a critical factor for both intelligent systems and risk management.


Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information

no code implementations12 Mar 2020 Zhize Wu, Thomas Weise, Le Zou, Fei Sun, Ming Tan

Differing from the previous studies, we propose a new method called Denoising Autoencoder with Temporal and Categorical Constraints (DAE_CTC)} to study the skeletal representation in a view of skeleton reconstruction.

Action Recognition Denoising +2

Learning in the Frequency Domain

4 code implementations CVPR 2020 Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, Fengbo Ren

Experiment results show that learning in the frequency domain with static channel selection can achieve higher accuracy than the conventional spatial downsampling approach and meanwhile further reduce the input data size.

Instance Segmentation Semantic Segmentation

SDM: Sequential Deep Matching Model for Online Large-scale Recommender System

2 code implementations1 Sep 2019 Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng

In this paper, we propose a new sequential deep matching (SDM) model to capture users' dynamic preferences by combining short-term sessions and long-term behaviors.

Collaborative Filtering Recommendation Systems

Privileged Features Distillation at Taobao Recommendations

no code implementations11 Jul 2019 Chen Xu, Quan Li, Junfeng Ge, Jinyang Gao, Xiaoyong Yang, Changhua Pei, Fei Sun, Jian Wu, Hanxiao Sun, Wenwu Ou

To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.

Improving Multi-turn Dialogue Modelling with Utterance ReWriter

1 code implementation ACL 2019 Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, Jie zhou

To properly train the utterance rewriter, we collect a new dataset with human annotations and introduce a Transformer-based utterance rewriting architecture using the pointer network.

Coreference Resolution Dialogue Rewriting

Deep Session Interest Network for Click-Through Rate Prediction

6 code implementations16 May 2019 Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang

Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt, Deep Session Interest Network(DSIN)

Click-Through Rate Prediction Recommendation Systems

POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion

1 code implementation6 May 2019 Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, Binqiang Zhao

In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion outfits, and the Personalization in the recommendation process.

Strain engineering of epitaxial oxide heterostructures beyond substrate limitations

no code implementations3 May 2019 Xiong Deng, Chao Chen, Deyang Chen, Xiangbin Cai, Xiaozhe Yin, Chao Xu, Fei Sun, Caiwen Li, Yan Li, Han Xu, Mao Ye, Guo Tian, Zhen Fan, Zhipeng Hou, Minghui Qin, Yu Chen, Zhenlin Luo, Xubing Lu, Guofu Zhou, Lang Chen, Ning Wang, Ye Zhu, Xingsen Gao, Jun-Ming Liu

The limitation of commercially available single-crystal substrates and the lack of continuous strain tunability preclude the ability to take full advantage of strain engineering for further exploring novel properties and exhaustively studying fundamental physics in complex oxides.

Materials Science

Compositional Network Embedding

no code implementations17 Apr 2019 Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang

Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.

Link Prediction Network Embedding +1

Set-valued risk statistics with the time value of money

no code implementations16 Apr 2019 Fei Sun, Xiaozhi Fan, Weitao Liu

The time value of money is a critical factor not only in risk analysis, but also in insurance and financial applications.

Regulator-based risk statistics for portfolios

no code implementations16 Apr 2019 Xiaochuan Deng, Fei Sun

Risk statistic is a critical factor not only for risk analysis but also for financial application.

Regulator-based risk statistics with scenario analysis

no code implementations16 Apr 2019 Xiaochuan Deng, Fei Sun

As regulators pay more attentions to losses rather than gains, we are able to derive a new class of risk statistics, named regulator-based risk statistics with scenario analysis in this paper.

Personalized Re-ranking for Recommendation

1 code implementation15 Apr 2019 Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.

Recommendation Systems Re-Ranking

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

7 code implementations14 Apr 2019 Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang

To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.

Sequential Recommendation

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning +1

ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation

1 code implementation CVPR 2019 Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, Peter Vajda, Matt Uyttendaele, Niraj K. Jha

We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.

Bayesian Optimization Efficient Neural Network +1

Multi-Source Pointer Network for Product Title Summarization

no code implementations21 Aug 2018 Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang

For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.

Sentence Summarization

Dynamic risk measures with markets volatility

no code implementations4 Jun 2018 Fei Sun, Jieming Zhou

In this paper, we extend to a special position space the convex risk measures introduced in F\"{o}llmer and Schied \cite{13} and the dynamic convex risk measures introduced in Detlefsen and Scandolo \cite{9}.

Semantic Regularities in Document Representations

no code implementations24 Mar 2016 Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, Xue-Qi Cheng

Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language.

Component-Enhanced Chinese Character Embeddings

no code implementations EMNLP 2015 Yan-ran Li, Wenjie Li, Fei Sun, Sujian Li

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English.

General Classification text-classification +3

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