xiangnan he ∗

Xiangnan He is on Facebook. However, most existing methods focused only on leveraging network structure. next > Department 54 DEPT OF COMPUTER SCIENCE; 1 DEPT OF ELECTRICAL & COMPUTER ENGG; 1 INSTITUTE OF SYSTEMS SCIENCE; Subject 4 Collaborative Filtering; 4 Recommendation; 3 Attention mechanism; 3 Collaborative filtering; 3 Deep Learning. Home Xiangnan He. [code] Dataset. Xue, Feng He, Xiangnan Wang, Xiang Xu, Jiandong Liu, Kai Hong, Richang Download Collect. Join Facebook to connect with Xiangnan He and others you may know. Skip to search form Skip to main content Semantic Scholar. 2 State Key Laboratory of Fluid Power and Mechatronic System, Key Laboratory of Soft … NUS-WIDE. Learn more about blocking users. He, Xiangnan Tang, Jinhui Du, Xiaoyu Hong, Richang Ren, Tongwei Chua, Tat-Seng Download Collect. Some features of the site may not work correctly. University of Science and Technology of China (USTC) 2020.09 - 2024.06 (expected) D.Eng. A Sequential Meta-Learning Approach, Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation, Interactive Path Reasoning on Graph for Conversational Recommendation, Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems, Reinforced Negative Sampling over Knowledge Graph for Recommendation, Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation, Bilinear Graph Neural Network with Neighbor Interactions, Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning, Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure, Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation, KGAT: Knowledge Graph Attention Network for Recommendation, λOpt: Learn to Regularize Recommender Models in Finer Levels, Modeling Extreme Events in Time Series Prediction, Semi-supervised User Profiling with Heterogeneous Graph Attention Networks, Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference, Explainable Reasoning over Knowledge Graph Paths for Recommendation, A Simple Convolutional Generative Network for Next-item Recommendation, Fast Matrix Factorization with Non-Uniform Weights on Missing Data, Adversarial Personalized Ranking for Recommendation, Knowledge-aware Multimodal Dialog Systems, TEM: Tree-enhanced Embedding Model for Explainable Recommendation, An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data, Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures, NAIS: Neural Attentive Item Similarity Model for Recommendation, Neural Factorization Machines for Sparse Predictive Analytics, Item Silk Road: Recommending Items from Information Domains to Social Users, Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-level Attention, Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, A Generic Coordinate Descent Framework for Learning from Implicit Feedback, BiRank: Towards Ranking on Bipartite Graphs, Fast Matrix Factorization for Online Recommendation with Implicit Feedback, Context-aware Image Tweets Modelling and Recommendation, TriRank: Review-aware Explainable Recommendation by Modeling Aspects, Relating an Image Tweet’s Text and Images, Predicting the Popularity of Web 2.0 Items Based on User Comments, Comment-based Multi-View Clustering of Web 2.0 Items, School of Information Science and Technology, University of Science and Technology of China, SIGIR 2020 Workshop on Information Retrieval in Finance, China Conference on Information Retrieval, IEEE International Conference on Cloud Computing and Intelligence Systems, CIKM 2017 Workshop on Social Media Analytics for Smart Cities. He Xiangnan hexiangnan. Block or report user Block or report XiangnanHe. Xie, Y. Gao, X.N. NMF Multi-view clustering methods: 4. The system can't perform the operation now. School of Data Science Block user. View Xiangnan He's profile, machine learning models, research papers, and code. Xiangnan He. The following articles are merged in Scholar. Apple. Block user. Baselines. COSDISH . FSDH [code] TSH . FAQ About Contact • Sign In Create Free Account. It includes eight disciplines such as literature, science, medicine, education, management, engineering, economics, law, etc. Research Interests: Causal recommendation, conversational recommender system, and natural language processing. Add to Firefox. SVD 3. Get paid for your ML skills Log In/Sign Up Xiangnan He Contact author. Optical Systems Algorithms Machine … Xiangnan He Experiments Baseline Methods for Comparison Single-view clustering methods (running on the combined view): 1. Xiangnan He, Key Laboratory of Nonlinear Science of Chinese Ministry of Education, School of Mathematical Sciences, Fudan University, Shanghai, P.R. Publications 13. h-index 4. Please login to be able to save your searches and receive alerts for new content matching your search criteria. Xiangnan University is a comprehensive university focusing on teachers training and medical education. Facebook gives people the power to share and makes the world more open and connected. K-means 2. 89 Results for: Author: Xiangnan He Edit Search Save Search Failed to save your search, try again later Search has been saved (My Saved Searches) Save this search. He is a Professor with the University of Science and Technology of China (USTC). Two full papers from my USTC group are accepted by, Two papers are accepted by IEEE Transactions on Knowledge and Data Engineering (, Five full research papers are accepted by, I am invited to be a program committee member in, One poster paper advised by me is accepted by, Three full papers advised by me are accepted by, Our tutorial proposal on "Deep Learning for Matching in Search and Recommendation" is accepted by, Conversational Recommendation: Formulation, Methods, and Evaluation, Learning and Reasoning on Graph for Recommendation, Deep Learning for Matching in Search and Recommendation, Recommendation Technologies for Multimedia Content, On the Equivalence of Decoupled Graph Convolution Network and Label Propagation, Disentangling User Interest and Conformity for Recommendation with Causal Embedding, Learning Intents behind Interactions with Knowledge Graph for Recommendation, Denoising Implicit Feedback for Recommendation, Bias and Debias in Recommender System: A Survey and Future Directions, Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users, LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, How to Retrain Recommender System? View ORCID Profile Xiangnan He 1, View ORCID Profile Chao Yuan 4, View ORCID Profile Ji Liu 1, View ORCID Profile Shlomo Magdassi 6 and ; View ORCID Profile Shaoxing Qu 2, † 1 Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China. student in School of Information Science and Technology. MNIST. Xiangnan He, School of Data Science, University of Science and Technology of China, I lead the USTC Lab for Data Science. Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Main track. Zhou Webpage template borrows from Weinan Zhang. Download: RESUME / 中文简历. University of Science and Technology of China. Recommender systems, information retrieval, applied machine learning. He, W. Xiong, P. Hilger, L. Jiang, and Y. F. Lu, “Plasmonic-Enhanced Carbon Nanotube Infrared Bolometers ”, Nanotechnology, 24, 035502 (2013) 2013 : M.M. Xiangnan He, 何向南, Professor in University of Science and Technology. WSDM 2009): extending LDA for clustering webpages from content words and Delicious tags. Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. arxiv.org — Authors:Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua (Submitted on 20 May 2019 (v1), last revised 8 Jun 2019 (this version, v2))Abstract: To provide more accurate, diverse, and explainable recommendation, it iscompulsory to go beyond modeling user-item interactions and take sideinformation into account. Prevent this user from interacting with your repositories and sending you notifications. Xiangnan He National University of Singapore xiangnanhe@gmail.com Lianhai Miao Hunan University lianhaimiao@gmail.com Yahui An University of Electronic Science and Technology of China anyahui.120@gmail.com Chao Yang∗ Hunan University yangchaoedu@hnu.edu.cn Richang Hong Hefei University of Technology hongrc.hfut@gmail.com ABSTRACT Due to the prevalence of group activities … Co-regularized Spectral Clustering (CoSC, Kumar et al. Read Xiangnan He's latest research, browse their coauthor's research, and play around with their algorithms SIGIR 2018 FSSH. Multi-Multinomial LDA (MMLDA, Remage et al. Semantic Scholar profile for Xiangnan He, with 16 highly influential citations and 13 scientific research papers. Xin Luo, Liqiang Nie, Xiangnan He Ye Wu, Zhen-Duo Chen, Xin-Shun Xu. Xiangnan He received the Ph.D. degree in computer science from the National University of Singapore (NUS), in 2016. News 30 Dec 2020 One paper is accepted by TOIS, on conversational recsys for cold users with EE tradeoff. Advisor: Xiangnan HE … Xiangnan He. Xiangnan He is on Facebook. Search for Xiangnan He's work. NAIS: Neural Attentive Item Similarity Model for Recommendation. A Data Science, Machine Learning, Deep Learning, Computer Vision Enthusiast and A Hiker :) Follow. Proceedings of the … Xiangnan He The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. Get our free extension to see links to code for papers anywhere online! Try again later. Hefei, China. Block user Report abuse. See more researchers and engineers like Xiangnan He. Nanjing University of Science and Technology, Nanjing, China, Jinhui Tang. He, L. Jiang, and Y.F. Education. 16 Nov 2020 One full paper is accepted by WSDM, on denoising implicit data for recsys. School of Information Science and Technology Prevent this user from interacting with your repositories and sending you notifications. Their, This "Cited by" count includes citations to the following articles in Scholar. Search Search. Advisor: Xiangnan HE (何向南). Xiangnan He National University of Singapore, Singapore xiangnanhe@gmail.com Lizi Liao National University of Singapore, Singapore liaolizi.llz@gmail.com Hanwang Zhang Columbia University USA hanwangzhang@gmail.com Liqiang Nie Shandong University China nieliqiang@gmail.com Xia Hu Texas A&M University USA hu@cse.tamu.edu Tat-Seng Chua National University of Singapore, Singapore … chongming.gao@email.com. Verified email at apple.com. Nanjing University of Science and Technology, Nanjing, China, Tat-Seng Chua. Last update: Dec 1, 2020. Zhou, Z.Q. Join Facebook to connect with He Xiangnan and others you may know. Search. Lu, “Seed-Free Growth of Diamond Patterns on Silicon Predefined by Femtosecond Laser Direct Writing”, Crystal Growth and Design, 13, 716-722(2013) 2013 : Y. Gao, Y.S. FSSH_deep As a byproduct, we have released the codes and parameter settings to facilitate other researchers. SDH . Add to Chrome. Wang, Y.S. Xiangnan He. Follow. You are currently offline. Research Fellow with School of Computing, National University of Singapore. 51 Xiangnan He; 42 Tat-Seng Chua; 17 Xiang Wang; 10 Liqiang Nie; 8 Fuli Feng. Xiangnan He. The ones marked, X He, L Liao, H Zhang, L Nie, X Hu, TS Chua, Proceedings of the 26th international conference on world wide web, 173-182, Proceedings of the 39th international ACM SIGIR conference on Research …, Proceedings of the 40th international ACM SIGIR conference on Research …, J Chen, H Zhang, X He, L Nie, W Liu, TS Chua, Proceedings of the 40th International ACM SIGIR conference on Research and …, J Xiao, H Ye, X He, H Zhang, F Wu, TS Chua, Proceedings of the Twenty-Sixth International Joint Conference on Artificial …, Proceedings of the 24th ACM International Conference on Information and …, Proceedings of the 42th international ACM SIGIR conference on Research …, IEEE Transactions on Knowledge and Data Engineering, H Zhang, F Shen, W Liu, X He, H Luan, TS Chua, X He, Z He, J Song, Z Liu, YG Jiang, TS Chua, IEEE Transactions on Knowledge and Data Engineering 30 (12), 2354-2366, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, The 41st International ACM SIGIR Conference on Research & Development in …, TSC Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Proceedings of the Twenty-Seventh International Joint Conference on …, Proceedings of the 26th international conference on World Wide Web, Proceedings of the 27th international conference on World Wide Web (WWW'18), X Wang, D Wang, C Xu, X He, Y Cao, TS Chua, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence …, Proceedings of the 56th Annual Meeting of the Association for Computational …, Z Cheng, Y Ding, X He, L Zhu, X Song, M Kankanhalli, L Zhu, Z Huang, X Liu, X He, J Sun, X Zhou, IEEE Transactions on Multimedia 19 (9), 2066-2079, New articles related to this author's research, University of Science and Technology of China, Fast Matrix Factorization for Online Recommendation with Implicit Feedback, Neural Factorization Machines for Sparse Predictive Analytics, Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention, Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks, TriRank: Review-Aware Explainable Recommendation By Modeling Aspects, Nais: Neural attentive item similarity model for recommendation, KGAT: Knowledge Graph Attention Network for Recommendation, Adversarial personalized ranking for recommendation, Item Silk Road: Recommending Items from Information Domains to Social Users, Outer Product-based Neural Collaborative Filtering, A Generic Coordinate Descent Framework for Learning from Implicit Feedback, TEM: Tree-enhanced Embedding Model for Explainable Recommendation, Explainable Reasoning over Knowledge Graphs for Recommendation, Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures, A3NCF: An Adaptive Aspect Attention Model for Rating Prediction, Discrete multimodal hashing with canonical views for robust mobile landmark search. Xiangnan He. I have over 70 pub Three variants of FSSH: FSSH_os FSSH_ts . LFH . Claim Author Page. View the profiles of people named He Xiangnan. TSC Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang. National University of Singapore, Singapore, Singapore. University of Science and Technology of China, Hefei, China, Zechao Li. Shallow models: KSH . Deep models: DSRH, DSCH, DRSCH, DPSH, … Learn more about blocking users. 5. Xiangnan He XiangnanHe Focusing. Xiangnan (Shawn) He, PhD | Cupertino, California | Machine Learning Engineer at Apple | 500+ connections | View Xiangnan (Shawn)'s homepage, profile, activity, articles My research interests span information retrieval, data mining, and multi-media analytics. He, Xiangnan He, Zhenkui Song, Jingkuan Liu, Zhenguang Jiang, Yu-Gang Chua, … Block or report user Block or report hexiangnan. His research interests span information retrieval, data mining, and multimedia analytics. Join Facebook to connect with Xiangnan He and others you may know. CIFAR-10. Deep Item-based Collaborative Filtering for Top-N Recommendation. Tongwei Chua, Tat-Seng Chua, Computer Vision Enthusiast and a Hiker: ) Follow, medicine, education management..., DPSH, … Xiangnan He and others you may know it includes eight disciplines such As literature,,... Tang, Jinhui Du, Xiang Wang ; 10 Liqiang Nie ; 8 Fuli Feng this user from with... Codes and parameter settings to facilitate other researchers Nie ; 8 Fuli Feng Hiker: ) Follow this `` by. Ml skills Log In/Sign Up Xiangnan He is on Facebook, Jinhui Tang Fuli Feng articles Scholar! Dec 2020 One paper is accepted by TOIS, on denoising implicit data for recsys makes the more. Implicit data for recsys, which is opposed to suggesting popular items He Xiangnan and you! Wang ; 10 Liqiang Nie ; 8 Fuli Feng learning, Deep learning, Deep learning, Computer Enthusiast... 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xiangnan he ∗ 2021