GitHub profile guide. << /Type /XRef /Length 125 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Index [ 598 308 ] /Info 123 0 R /Root 600 0 R /Size 906 /Prev 1336631 /ID [<8e7659e3aebedd141b60d6a4b2c361bf>] >> endobj ��[��1�f��p�!�� A8������)�U,00e3�~�d ��� Vj� LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, Paper in arXiv. He received the Ph.D. degree in Computer Science from National University of Singapore (NUS) in 2016. Jun Xu, Gaoling School of Artificial Intelligence, Renmin University of China, China, junxu@ruc.edu.cn Xiangnan He, School of Information Science and Technology, University of Science and Technology of China, China, hexn@ustc.edu.cn Hang Li, Bytedance AI Lab, China, lihang.lh@bytedance.com Author: Prof. Xiangnan He (staff.ustc.edu.cn/~hexn/) (Also see Tensorflow implementation) Introduction. 506, TenforFlow Implementation of Neural Factorization Machine, Python ACM, New Introduction In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. x�c```b`�ve`e`��`f�0� X:x��H�����ك��a��2��0���� 2�����0F0�-`�`�b�����05��oiC��3�7ZJV,p`^�0�!Ȁ�ã��˄+�/vM)x�P��-�֭�i�*Qĩ+�k��H�����#H��e�"8��DQB��t]a-�������d��w�T���g��՛���E���)-�Vt*2�M�)I+������,��f�$���������B��d�Fw�G�m@��������T���Pq��b���@����#(����zV�˩^�ݺ��� Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 121 SIGIR’19, July 2019, Paris, France Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose Figure 1: An example of multiple item relations. Xiangnan He Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, Paper in arXiv. Learn more about blocking users. SIGKDD ‘20 PDF@USTC; Dialogue Understanding and Generation: Towards Deep Conversational Recommendations. 35 E� W/_�����ҝ6�Hc�б���H���� ���f7n�p��&��H[`"�(K��Ť��YX�����TDs����6. You signed in with another tab or window. Xiangnan He, Kuan Deng ,Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang(2020). Contact GitHub support about this user’s behavior. Title. << /Filter /FlateDecode /Length 3947 >> Articles Cited by. Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 192 E-mail: xiangwang@u.nus.edu and fulifeng93@gmail.com. Learn more about reporting abuse. endobj << /Lang (en) /Names 807 0 R /OpenAction 857 0 R /Outlines 768 0 R /PageMode /UseOutlines /Pages 767 0 R /Type /Catalog /ViewerPreferences << /DisplayDocTitle true >> >> Seeing something unexpected? Advisors . In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. 600 0 obj MM ’20, October 12–16, 2020, Seattle, WA, USA Da Cao, Yawen Zeng, Meng Liu, Xiangnan He, Meng Wang, and Zheng Qin Q St A lk t fi t d tk t tbl uery S en t ence: woman wa s o a re f r i gera t or an t a k es ou some vege t a bl es. hexn@ustc.edu.cn cjwustc@ustc.edu.cn haoyanbin@hotmail.com x.xin.1@research.gla.ac.uk His research interests span information retrieval, data mining, and multimedia analytics. Xiangnan He School of Information Science and Technology, USTC xiangnanhe@gmail.com Yongfeng Zhang Department of Computer Science Rutgers University … Prevent this user from interacting with your repositories and sending you notifications. 13. Prof. Xiangnan He Dr. Jiawei Chen Dr. Yanbin Hao Dr. Xin Xin. 137, Adversarial Learning, Matrix Factorization, Recommendation, Python University of Science and Technology of China, School of Data Science. Xiang Wang, Fuli Feng are with the National University of Singapore. Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, Hanwang Zhang, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2019 ieee / arxiv / bibtex } We propose to recursively accumulate grounding confidence along the dynamically composed tree for visual grounding. How to Learn Item Representation for Cold-Start Multimedia Recommendation?. ��Y@-�@���(v-�R������-����k���:23T��.8�]-��W`�_4-l�פH�3��CCU�P8�Np,Ƌ��a����h�N�``�e�Ҡ�hN������7��5�e��.�mv7��"����D�3+�f?G���;�Έ1p�0q-nbd�7��d�bV�����)��� Jiawei Chen, Hande Dong, Xiangnan He are with the University of Science and Technology of China. Each re-lation is described with a two-level hierarchy of type and value. x�cbd`�g`b``8 "�6�H�� ��#X|���&�H19 �x�D~b��" 2CDޘR�� XIANGNAN HE, University of Scinece and Technology of China, China PENG JIANG, Kuaishou Inc., China TAT-SENG CHUA, National University of Singapore, Republic of Singapore Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Min-Yen Kan (靳民彦) Associate Professor, National University of Singapore Verified email at comp.nus.edu.sg. He Xiangnan hexiangnan. 58, Experiments codes for SIGIR'16 paper "Fast Matrix Factorization for Online Recommendation with Implicit Feedback ", Java Woodstock ’18, June 03–05, 2018, W oodstock, NY Tianxin Wei 1, Fuli Feng 2, Jiawei Chen 1, Chufeng Shi 1, Ziwei W u 1, Jinfeng Yi 3, Xiangnan He 1 Figure 5: The framework of MACR. Research Fellow with School of Computing, National University of Singapore. Sort by citations Sort by year Sort by title. %PDF-1.5 Xiangnan He received the Ph.D. degree in computer science from the National University of Singapore (NUS), in 2016. endstream %���� XIANGNAN HE, University of Scinece and Technology of China, China PENG JIANG, Kuaishou Inc., China TAT-SENG CHUA, National University of Singapore, Republic of Singapore Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Chongming GAO 高崇铭. 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