Xiangnan He. Recommender systems, information retrieval, applied machine learning. Home Xiangnan He. student in School of Information Science and Technology. Deep models: DSRH, DSCH, DRSCH, DPSH, … Prevent this user from interacting with your repositories and sending you notifications. View the profiles of people named He Xiangnan. 51 Xiangnan He; 42 Tat-Seng Chua; 17 Xiang Wang; 10 Liqiang Nie; 8 Fuli Feng. Xiangnan He is on Facebook. Nanjing University of Science and Technology, Nanjing, China, Tat-Seng Chua. My research interests span information retrieval, data mining, and multi-media analytics. University of Science and Technology of China, Hefei, China, Zechao Li. Publications 13. h-index 4. Webpage template borrows from Weinan Zhang. Xin Luo, Liqiang Nie, Xiangnan He Ye Wu, Zhen-Duo Chen, Xin-Shun Xu. News 30 Dec 2020 One paper is accepted by TOIS, on conversational recsys for cold users with EE tradeoff. FSDH [code] TSH . Xiangnan He. Their, This "Cited by" count includes citations to the following articles in Scholar. Xue, Feng He, Xiangnan Wang, Xiang Xu, Jiandong Liu, Kai Hong, Richang Download Collect. Xie, Y. Gao, X.N. He, W. Xiong, P. Hilger, L. Jiang, and Y. F. Lu, “Plasmonic-Enhanced Carbon Nanotube Infrared Bolometers ”, Nanotechnology, 24, 035502 (2013) 2013 : M.M. 2 State Key Laboratory of Fluid Power and Mechatronic System, Key Laboratory of Soft … Xiangnan (Shawn) He, PhD | Cupertino, California | Machine Learning Engineer at Apple | 500+ connections | View Xiangnan (Shawn)'s homepage, profile, activity, articles Get paid for your ML skills Log In/Sign Up Xiangnan He Contact author. 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. Add to Chrome. Verified email at apple.com. Wang, Y.S. Download: RESUME / 中文简历. 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. I have over 70 pub He, L. Jiang, and Y.F. TSC Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang. 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. Block user Report abuse. Proceedings of the … 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 … 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. Zhou, Z.Q. School of Data Science Follow. University of Science and Technology of China. Learn more about blocking users. The following articles are merged in Scholar. Some features of the site may not work correctly. He, Xiangnan Tang, Jinhui Du, Xiaoyu Hong, Richang Ren, Tongwei Chua, Tat-Seng Download Collect. Nanjing University of Science and Technology, Nanjing, China, Jinhui Tang. Xiangnan He received the Ph.D. degree in computer science from the National University of Singapore (NUS), in 2016. Xiangnan He. Xiangnan He, Key Laboratory of Nonlinear Science of Chinese Ministry of Education, School of Mathematical Sciences, Fudan University, Shanghai, P.R. NUS-WIDE. Please login to be able to save your searches and receive alerts for new content matching your search criteria. Deep Item-based Collaborative Filtering for Top-N Recommendation. LFH . He, Xiangnan He, Zhenkui Song, Jingkuan Liu, Zhenguang Jiang, Yu-Gang Chua, … Research Interests: Causal recommendation, conversational recommender system, and natural language processing. WSDM 2009): extending LDA for clustering webpages from content words and Delicious tags. 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. Join Facebook to connect with Xiangnan He and others you may know. Get our free extension to see links to code for papers anywhere online! Xiangnan He. Claim Author Page. Xiangnan He, School of Data Science, University of Science and Technology of China, I lead the USTC Lab for Data Science. K-means 2. Advisor: Xiangnan HE … Search Search. Baselines. Block user. Semantic Scholar profile for Xiangnan He, with 16 highly influential citations and 13 scientific research papers. Block or report user Block or report XiangnanHe. Advisor: Xiangnan HE (何向南). Xiangnan He, 何向南, Professor in University of Science and Technology. Search for Xiangnan He's work. Prevent this user from interacting with your repositories and sending you notifications. Xiangnan He The general aim of the recommender system is to provide personalized suggestions to users, which is opposed to suggesting popular items. Zhou SVD 3. A Data Science, Machine Learning, Deep Learning, Computer Vision Enthusiast and A Hiker :) Follow. NMF Multi-view clustering methods: 4. Xiangnan He Experiments Baseline Methods for Comparison Single-view clustering methods (running on the combined view): 1. FAQ About Contact • Sign In Create Free Account. 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. Shallow models: KSH . [code] Dataset. 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.