%PDF-1.5 Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, and Kazunari Sugiyama. endobj Xiangnan He University of Science and Technology of China, China [email protected] ABSTRACT Time series prediction is an intensively studied topic in data mining. endstream [email protected] [email protected] [email protected] [email protected] E-mail: [email protected], [email protected] and [email protected] 602 0 obj Research Fellow with School of Computing, National University of Singapore. SIGKDD ‘20 [email protected]; Dialogue Understanding and Generation: Towards Deep Conversational Recommendations. Xiangnan He, Kuan Deng ,Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang(2020). Xiangnan He School of Information Science and Technology, USTC [email protected] Yongfeng Zhang Department of Computer Science Rutgers University … He is a Professor with the University of Science and Technology of China (USTC). 70, Using theano to implement the Matrix Factorization with BPR ranking loss, Python 166, TensorFlow Implementation of Attentional Factorization Machine, Python x�cbd`�g`b``8 "�6�H�� ��#X|���&�H19 �x�D~b��" 2CDޘR�� E� W/_�����ҝ6�Hc�б���H���� ���f7n�p��&��H[`"�(K��Ť��YX�����TDs����6. Seeing something unexpected? Contributors: Dr. Xiangnan He (staff.ustc.edu.cn/~hexn/), Kuan Deng, Yingxin Wu. In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. 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. How to Retrain Recommender System? << /Annots [ 859 0 R 860 0 R 861 0 R 858 0 R ] /Contents 603 0 R /MediaBox [ 0 0 486 720 ] /Parent 753 0 R /Resources 864 0 R /Type /Page >> Block user. 443 Huangshan Road, Hefei, China 230027 Email: xiangnanhe at gmail.com I lead the USTC Lab for Data Science. 506, TenforFlow Implementation of Neural Factorization Machine, Python Prevent this user from interacting with your repositories and sending you notifications. 35 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. Title. Previous models largely follow a general supervised learning paradigm — treating each Articles Cited by. Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 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. Xiangnan He University of Science and Technology of China [email protected] Tat-Seng Chua National University of Singapore [email protected] ABSTRACT Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. 601 0 obj Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen & Tat-Seng Chua. 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. Dr. Xiangnan He is a professor with the University of Science and Technology of China (USTC). 599 0 obj ACM, New 233-243. Xiang Wang, Fuli Feng are with the National University of Singapore. 121 Prof. Xiangnan He Dr. Jiawei Chen Dr. Yanbin Hao Dr. Xin Xin. My research interests span information retrieval, data mining, and multi-media analytics. endobj Xiangnan He, Min-Yen Kan, Peichu Xie, and Xiao Chen. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2014, Accept rate: 21%), pp. GitHub profile guide. wenqiang lei National University of Singapore Verified email at u.nus.edu. Block or report user Block or report hexiangnan. Xiangnan He. x��:�r�F����ވ����*.�E��1��h$��f�����B�ܯ���,\DKޘ��}!QWV�W��9l���>=����ȍ�Z��&R��C��O�p^�"��M�o�bs��-l?��}w����d�n������v��@$��B�����On�y�����N�D�~��C�Ev*��-�;��O���7ՁF����1��hi&����+N��~QW���W4m֙��J�n��H�AD7�+�����ө��v����﮻��� "$ �"}���D�� 2020. << /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 >> >> Python Verified email at ustc.edu.cn - Homepage. Learn more about blocking users. PDF Slides [9] Comment-based Multi-View Clustering of Web 2.0 Items. endobj Jiawei Chen, Hande Dong, Xiangnan He are with the University of Science and Technology of China. Research Fellow with School of Computing, National University of Singapore. Contributors: Dr. Xiangnan He (staff.ustc.edu.cn/~hexn/), Kuan Deng, Yingxin Wu. 13. 598 0 obj This version of CS6101 is jointly run by [email protected] and Lab of Data Science @ USTC, the latter one is led by our WING alumnus Xiangnan He. 600 0 obj He received the Ph.D. degree in Computer Science from National University of Singapore (NUS) in 2016. How to Learn Item Representation for Cold-Start Multimedia Recommendation?. yet for this period. 1.2k NIPS ‘18 [email protected] 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. Take a look at the endobj stream A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. << /Linearized 1 /L 1340490 /H [ 2885 753 ] /O 602 /E 396037 /N 29 /T 1336630 >> Xiangnan He Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System Tianxin Wei1, Fuli Feng2, Jiawei Chen1, Chufeng Shi1, Ziwei Wu1, Jinfeng Yi3, Xiangnan He1 1University of Science and Technology of China, 2National University of Singapore, 3JD AI Research [email protected],[email protected],[email protected] E-mail: [email protected] and [email protected] ��[��1�f��p�!�� A8������)�U,00e3�~�d ��� Vj� Follow. Introduction In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation. Chongming GAO 高崇铭. Contributors: Dr. Xiangnan He (staff.ustc.edu.cn/~hexn/), Kuan Deng, Yingxin Wu. University of Science and Technology of China, School of Data Science. 603 0 obj ��[email protected]�@���(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�����)��� 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. "Yրɵ`�+��`r1���w�����.�F�Qr��&�;w��0J�q���a��$ Gv: 416 endobj Sort. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation, Paper in arXiv. Xiangnan He received the Ph.D. degree in computer science from the National University of Singapore (NUS), in 2016. 356 Min-Yen Kan (靳民彦) Associate Professor, National University of Singapore Verified email at comp.nus.edu.sg. 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