删除或更新信息,请邮件至freekaoyan#163.com(#换成@)

中国科学技术大学大数据学院导师教师师资介绍简介-何向南

本站小编 Free考研考试/2021-04-24




Email: hexn@ustc.edu.cn
个人主页: http://staff.ustc.edu.cn/~hexn/
地址:School of Information Science and Technology School of Data Science(under construction) University of Science and Technology of China 443 Huangshan Road, Hefei, China 230027

主要研究兴趣:
My research interests span information retrieval, data mining, and multi-media analytics. I have over 60 publications appeared in several top conferences such as SIGIR, WWW, KDD, and MM, and journals including TKDE, TOIS, and TMM. My work on recommender systems has received the Best Paper Award Honourable Mention in WWW 2018 and ACM SIGIR 2016. Moreover, I have served as the (senior) PC member for several top conferences including SIGIR, WWW, KDD, MM etc., and the regular reviewer for journals including TKDE, TOIS, TMM, etc.

招生信息:
If you are in the academia job market: theSchool of Data Science (大数据学院)of USTC is actively hiring tenure-track faculties and postdocs. We provide competitive salary, sufficient funding and student supports, and good career opportunities. Please approach me if you are interested in joining USTC or working with me!

教育经历:

Sep 2007 - June 2011,Bachelor in Software Engineering,East China Normal University (ECNU),Shanghai, China
July 2011 - April 2016,Ph.D. in Computer Science,National University of Singapore (NUS), Singapore


研究经历:
April 2019 - Present,Professor, University of Science and Technology of China
May 2016 - March 2019,Postdoc Research Fellow, National University of Singapore
June 2015 - Sep 2015,Research Intern, Google Research (Mountain View)
June 2014 - Sep 2014,Software Engineering Intern, Google (New York)
Sep 2010 - Dec 2010, Software Engineering Intern, Microsoft (Shanghai)

主要论著:
[01]Wang X, He X, Wang M, et al. Neural Graph Collaborative Filtering[C].SIGIR 2019.
[02]Xin X, He X, Zhang Y, et al. Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation[C]. SIGIR 2019.
[03]Yang X, He X, Wang X, et al. Interpretable Fashion Matching with Rich Attributes[C]. SIGIR 2019.
[04]Wang X, He X, Cao Y, et al. KGAT: Knowledge Graph Attention Network for Recommendation[C]. KDD 2019
[05]Hu H, He X. Sets2Sets: Learning from Sequential Sets with Neural Networks[C].KDD 2019.
[06]Chen Y, Chen B, He X, et al. Lambda Opt: Learn to Regularize Recommender Models in Finer Levels[C]. KDD 2019.
[07]Ding D, Zhang M, Pan X, et al. Modeling Extreme Events in Time Series Prediction[C]. KDD 2019.
[08]Ding J, Quan Y, He X, et al.Reinforced Negative Sampling for Recommendation with Exposure Data[C].IJCAI 2019.
[09]Xin X, Chen B, He X, et al. CFM: Convolutional Factorization Machines for Context-Aware Recommendation[C].IJCAI 2019
[10]Chen L, Liu Y, He X, et al. Matching User with Item Set: Collaborative Bundle Recommendation with Attention Network[C].IJCAI 2019.
[11]Feng F, Chen H, He X, et al.EnhancingStock Movement Prediction with Adversarial Training[C]. IJCAI 2019.
[12]Chen W, Gu Y, Ren Z, et al. Semi-supervised User Profiling with Heterogeneous Graph Attention Networks[C]. IJCAI 2019.
[13]Cao Y, Wang X, He X, et al. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences[C]//WWW 2019: 151-161.
[14]Gao C, Chen X, Feng F, et al. Cross-domain Recommendation Without Sharing User-relevant Data[C]//WWW 2019: 491-502.
[15]Wang X, Wang D, Xu C, et al. Explainable Reasoning over Knowledge Graphsfor Recommendation[C]. AAAI 2019.
[16]Li X, Song J, Gao L, et al. Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering[C]. AAAI 2019.
[17]Yuan F, Karatzoglou A, Arapakis I, et al. A Simple Convolutional Generative Network for Next Item Recommendation[C]//WSDM 2019: 582-590.
[18]Gao C, He X, Gan D, et al. Neural Multi-Task Recommendation from Multi-Behavior Data[C]//ICDE (Short).2019.
[19]Feng F, He X, Tang J, et al. Graph Adversarial Training: DynamicallyRegularizing Based on Graph Structure[J]. IEEE Transactions on Knowledge andData Engineering (TKDE, under submission).
[20]Gao M, He X, Chen L, et al. Learning Vertex Representations for Bipartite Networks[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE, undersubmission).
[21]Gao X, Feng F, He X, et al. Visually-aware Collaborative Food Recommendation[J]. IEEE Transactions on Multimedia (TMM, under submission).
[22]Hong R, Liu D, Mo X, et al. Learning to Compose and Reason with LanguageTree Structures for Visual Grounding[J]. IEEE transactions on pattern analysisand machine intelligence 2019.
[23]Feng F, He X, Wang X, et al. Temporal Relational Ranking for Stock Prediction[J].ACM Transactions on Information Systems (TOIS) 2019, 37(2): 27.
[24]Guan X, Cheng Z, He X, et al. Attentive Aspect Modeling for Review-aware Recommendation[J]. ACM Transactions on Information Systems (TOIS) 2019, 37(3):28.
[25]He X, Tang J, Du X, et al.Fast Matrix Factorization with Non-Uniform Weights on Missing Data[J]. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2019
[26]Tang J, Du X, He X, et al. Adversarial training towards robust multimedia recommender system[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2019.
[27]Ding J, Yu G, He X, et al. SamplerDesign for Bayesian Personalized Ranking by Leveraging View Data[J]. IEEETransactions on Knowledge and Data Engineering (TKDE, Major Revision) 2019
[28]Liu Y, Li Z, Zhou C, et al. Generative adversarial active learning for unsupervised outlier detection[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2019.
[29]Xue F, He X, Wang X, et al. Deep Item-based Collaborative Filtering for Top-N Recommendation[J].ACM Transactions on Information Systems (TOIS) 2019,37(3): 33.
[30]He X, He Z, Du X, et al. Adversarial personalized ranking for recommendation[C]//SIGIR 2018: 355-364.
[31]Gao M, Chen L, He X, et al. BiNE: Bipartite Network Embedding[C]//SIGIR 2018: 715-724.
[32]Cao D, He X, Miao L, et al. Attentive group recommendation[C]//SIGIR 2018: 645-654.
[33]Luo X, Nie L, He X, et al. Fast Scalable Supervised Hashing[C]//SIGIR 2018: 735-744.
[34]Song X, Wang X, Nie L, et al. A Personal Privacy Preserving Framework: ILet You Know Who Can See What[C]//SIGIR 2018: 295-304.
[35]Liu M, Wang X, Nie L, et al. Attentive moment retrieval in videos[C]// SIGIR 2018: 15-24.
[36]Liao L, Ma Y, He X, et al. Knowledge-aware Multimodal Dialogue Systems[C]//MM 2018:801-809.(Best Paper Final List)
[37]Gelli F, Uricchio T, He X, et al. Beyond the Product: Discovering Image Posts for Brands in Social Media[C]//MM 2018.
[38]Liao L, He X, Zhao B, et al. Interpretable multimodal retrieval for fashion products[C]//MM 2018
[39]Yu W, Zhang H, He X, et al. Aesthetic-based clothing recommendation[C]//WWW 2018(Best Paper Award Honorable Mention)
[40]Wang X, He X, Feng F, et al. Tem: Tree-enhanced embedding model for explainable recommendation[C]//WWW 2018 : 1543-1552.
[41]Feng F, He X, Liu Y, et al. Learning on partial-order hypergraphs[C]//WWW 2018:1523-1532.
[42]Ding J, Feng F, He X, et al. An improved sampler for bayesian personalized ranking by leveraging view data[C]//WWW 2018 (Poster): 13-14.(Best Poster Award)
[43]Yuan F, Xin X, He X, et al. fBGD: Learning embeddings from positive unlabeled data with BGD[C]. UAI 2018.
[44]He X, Du X, Wang X, et al. Outer product-based neural collaborative filtering[C].IJCAI 2018.
[45]Liu H, He X, Feng F, et al. Discrete factorization machines for fastfeature-based recommendation[C].IJCAI 2018.
[46]Ding J, Yu G, He X, et al. Improving Implicit Recommender Systems with View Data[C]//IJCAI 2018: 3343-3349.
[47]Cheng Z, Ding Y, He X, et al. A^ 3NCF: An Adaptive Aspect Attention Modelfor Rating Prediction[C]//IJCAI 2018: 3748-3754.
[48]Shen T, Jia J, Shen G, et al. Cross-Domain Depression Detection via Harvesting Social Media[C]//IJCAI 2018: 1611-1617.
[49]Xin X, Yuan F, He X, et al. AllVec: Learning Word Representations Without Negative Sampling[C]. ACL 2018.
[50]Lei W, Jin X, Kan M Y, et al. Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures[C]//ACL 2018: 1437-1447.
[51]Liao L, He X, Zhang H, et al. Attributed social network embedding[J].IEEE Transactions on Knowledge and Data Engineering (TKDE) 2018, 30(12): 2257-2270.
[52]Zhang D, Guo L, He X, et al. A graph-theoretic fusion framework for unsupervised entity resolution[C]//2018 IEEE 34th International Conference onData Engineering (ICDE). IEEE, 2018: 713-724.
[53]He X, He Z, Song J, et al. NAIS: Neural attentive item similarity model for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE) 2018, 30(12): 2354-2366.
[54]Chen J, He X, Song X, et al. Venue prediction for social images by exploiting rich temporal patterns in lbsns[C]/MMM 2018 (Poster): 327-339.
[55]Gao Z, Wang D, He X, et al. Group-pair convolutional neural networks formulti-view based 3d object retrieval[C]//AAAI 2018.
[56]He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]/ SIGIR 2017: 355-364.
[57]Wang X, He X, Nie L, et al. Item silk road: Recommending items from information domains to social users[C]// SIGIR 2017: 185-194.
[58]Chen J, Zhang H, He X, et al.Attentive Collaborative Filtering: Multimedia Recommendation with Feature- and Item-levelAttention[C]. SIGIR 2017.
[59]Cao D, Nie L, He X, et al. Embedding factorization models for jointly recommending items and user generated lists[C]//SIGIR 2017: 585-594.
[60]Gelli F, He X, Chen T, et al. How personality affects our likes: Towardsa better understanding of actionable images[C]//MM 2017: 1828-1837.
[61]Nie L, Wang X, Zhang J, et al. Enhancing micro-video understanding byharnessing external sounds[C]//MM 2017: 1192-1200.
[62]Xu D, Zhao Z, Xiao J, et al. Video question answering via gradually refined attention over appearance and motion[C]//MM 2017: 1645-1653.
[63]Liu Z, Cheng L, Liu A, et al. Multiview and multimodal pervasive indoor localization[C]//MM 2017: 109-117.
[64]Zhu L, Huang Z, Liu X, et al. Discrete multi-modal hashing with canonicalviews for robust mobile landmark search[J].IEEE Transactions on Multimedia(TMM) 2017, 19(9): 2066-2079.
[65]Xiao J, Ye H, He X, et al. Attentional factorization machines: Learningthe weight of feature interactions via attention networks[C].IJCAI 2017.
[66]Liao L, He X, Ren Z, et al. Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media[C]//IJCAI. 2017: 310-316.
[67]Lei W, Wang X, Liu M, et al. SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition[C]//IJCAI. 2017: 4026-4032.
[68]He X, Liao L, Zhang H, et al. Neural collaborativefiltering[C]//WWW 2017:173-182.
[69]Bayer I, He X, Kanagal B, et al. A generic coordinate descent frame workfor learning from implicit feedback[C]//WWW 2017: 1341-1350.
[70]He X, Gao M, Kan M Y, et al. Birank: Towards ranking on bipartite graphs[J]. IEEE Transactions on Knowledge and Data Engineering,(TKDE) 2016, 29(1):57-71.
[71]Cao D, He X, Nie L, et al. Cross-platform app recommendation by jointly modeling ratings and texts[J]. ACM Transactions on Information Systems (TOIS) 2017, 35(4): 37.
[72]Cao D, Nie L, He X, et al. Version-sensitive mobile Apprecommendation[J]. Information Sciences, 2017, 381: 161-175.
[73]He X, Zhang H, Kan M Y, et al. Fast matrix factorization for online recommendation with implicit feedback[C]//SIGIR 2016: 549-558.
[74]Zhang H, Shen F, Liu W, et al. Discrete collaborative filtering[C]//SIGIR 2016: 325-334.(Best Paper Award Honorable Mention)
[75]Chen T, He X, Kan M Y. Context-aware image tweet modelling and recommendation[C]//MM 2016: 1018-1027.
[76]Zhang J, Nie L, Wang X, et al. Shorter-is-better: Venue category estimation from micro-video[C]//MM 2016: 1415-1424.
[77]He X, Chen T, Kan M Y, et al. Trirank: Review-aware explainable recommendation by modeling aspects[C]//CIKM 2015:1661-1670.
[78]Chen T, SalahEldeen H M, He X, et al. VELDA: Relating an Image Tweet's Text and Images[C]//AAAI. 2015: 30-36.
[79]He X, Gao M, Kan M Y, et al. Predicting the popularity of web 2.0 itemsbased on user comments[C]//SIGIR 2014:233-242.
[80]He X, Kan M Y, Xie P, et al. Comment-based multi-view clustering of web2.0 items[C]//WWW 2014: 771-782.
[81]Jin Y, Kan M Y, Ng J P, et al. Mining scientific terms and their definitions: A study of the ACL anthology[C]//EMNLP 2013: 780-790.
[82]Gao M, He X, Jin C, et al. Recording how-provenance on probabilistic databases[C]//APWEB 2010:205-211.
[83]Xu J, He X, Li H. Deep learning for matching in search and recommendation[C]//SIGIR 2018: 1365-1368.
[84]Ren Z, He X, Yin D, et al. InformationDiscovery in E-commerce[C].SIGIR 2018
[85]Xu J, He X, Li H. Deep learning for matching in search and recommendation[C]//SIGIR 2018: 1365-1368.
[86]He X, Zhang H, Chua T S. Recommendation Technologies for Multimedia Content[C]//ICMR. 2018: 8.









相关话题/中国科学技术大学 数据

闂佽瀛╅鏍窗閹烘纾婚柟鍓х帛閳锋垶銇勯幇鍓佹偧闁硅棄鐡闂傚倷鐒︾€笛呯矙閹烘鍎楁い鏂垮⒔閸楁岸鏌涢幘妤€鎳愰悡瀣倵楠炲灝鍔氶柣妤€鍟村畷鎴﹀箻鐠囪尙顦ㄥ銈呯箰濡鎮″☉銏♀拺闁稿繐鍚嬬欢鏌ユ煕閻旂ǹ鈻曢柟顖欑劍瀵板嫮浠﹂幆褎鐎梻浣虹《閸撴繃绗熷Δ鍛辈闁靛鏅滈埛鎺楁煕濞戝崬骞樼紒鐙呯悼缁辨帞浠﹂悾灞界厽闂佽鍨板ú銊╁焵椤掑﹦鍒板褍娴风划鍫熷緞鐎n偄寮块梺鎸庣箓閹虫劗绮婚崣澶岀瘈闁逞屽墴閹囧醇閳垛晜鐏冮柣搴$畭閸庨亶骞婃惔銊﹀亗闁稿本鍩冨Σ鍫ユ煙缂併垹鐏犲ù婊堢畺濮婃椽宕ㄦ繝鍌滅懆濠碘槅鍋呯换鍫ョ嵁閹扮増鏅搁柨鐕傛嫹
547闂傚倷绀佸﹢閬嶃€傛禒瀣;闁瑰墽绮埛鎺楁煕閺囨娅呴柣蹇d邯閺岋絽螖閳ь剟鏌婇敐澶婄疇闁规崘顕х粈鍐┿亜韫囨挻顥滈柣鎾寸洴濮婃椽宕楅崗鑲╁嚒闂佸摜鍣ラ崑鍕偩閻戣姤鏅搁柨鐕傛嫹1130缂傚倸鍊风粈渚€藝椤栨粎鐭撻柣銏㈩暯閸嬫捇宕归锝囨闂侀€炲苯澧叉い顐㈩槺閸犲﹤顓兼径濠冭緢闂侀€炲苯澧撮柡灞稿墲瀵板嫮鈧綆浜滈~搴♀攽閻愯尙澧涢柛銊ョ仢閻g兘鏁撻悩鑼槰閻熸粌绻掔划娆撳炊閳哄啰锛滄繝銏f硾閿曪附鏅ラ梻浣告啞鑿ч柛濠冪墱缂傛捇鎸婃竟鈺傛瀹曨亝鎷呯憴鍕В闂備礁婀遍崢褔鎮洪妸鈺佺闁割偅娲栭悞鍨亜閹寸偛顕滅紒浣哄閵囧嫰顢曢姀鈺佸壉闂佹寧绋掗崝娆撶嵁鐎n噮鏁嶆繝濠傛媼濡查攱绻濋悽闈涗沪闁搞劌缍婂顐ょ矙濡數鍎ょ换婵嬪炊閵娿儲顓洪梺鍝勵槸閻楁粓宕戦幒妤€鍚归柡鍐ㄧ墛閻撳繘鏌涢埄鍐╃闁稿繐鐬肩槐鎺楊敊閸忓浜鹃悺鎺嶆祰椤骞嗛弬搴撴闁圭儤鍨虫竟鏇熺箾鏉堝墽鎮奸柡鍜佸亜鍗遍柛顐犲劜閻撴洟鏌熼悜妯诲鞍闁稿濞€閺屽秷顧侀柛蹇旂〒濞嗐垹顫濈捄娲7婵°倧绲介崯顖炲箠濮樻墎鍋撻獮鍨姎闁哥喓濞€瀹曟垿骞樼€涙ê顎撻梺缁樺灦閿氶柍褜鍓涢崑鐔煎焵椤掑喚娼愰柟顔肩埣瀹曟洟顢涢悙鑼姦濡炪倖鍨奸崕濠氬礂鐏炰勘浜滈柟鎯х摠閸婃劙鏌熼鑲╁煟鐎规洟浜堕獮鍥Ω閵夈倕顥氬┑鐐舵彧缁茬偓绂嶉懞銉ь浄闁挎洍鍋撻棁澶愭煕韫囨洖甯堕柟鍏兼倐閺屽秷顧侀柛蹇旂☉闇夐柛銉墮绾惧ジ鏌曟繝蹇曠暠妞ゆ洝椴搁幈銊ノ熺粙鍨闂佺ǹ顑嗛幐濠氬箯閸涱垱鍠嗛柛鏇ㄥ墰閺嗘岸姊绘担鍝勫付鐎殿喗鍎奸妵鎰板礃椤忓棛澶勬俊銈忕到閸燁垶宕戦悩缁樼厱闁斥晛鍟慨鈧梺绋款儐閹稿骞忛崨瀛樺仾妞ゆ牗鑹剧粻鎴︽⒒娴e憡鎯堥柛濠勄圭叅闁靛繈鍊曢悞鍨亜閹寸偛顕滅紒浣规緲椤法鎲撮崟顒€纾抽悗娈垮枤閺佸銆佸☉妯滅喎鐣℃0浣割棜濠电偠鎻紞鈧い顐㈩樀閹繝鍩€椤掑嫭鈷掗柛灞捐壘閳ь兛绮欓、娆愮節閸曨剦娼熼梺鍓插亝濞叉牜绮荤紒妯镐簻闁圭偓娼欓ˉ姘舵煕鐎n偅灏伴柟宄版嚇瀹曠兘顢樺┃鎯т壕濠电姴娲﹂崐鍨箾閹存繄鏋冪紒鈧€n喗鐓冪憸婊堝礂濞戞氨鐭嗗ù锝堟〃閻掑﹪鏌熷▓鍨灀闁稿鎸搁埥澶屸偓鍦Х椤︿即姊洪柅鐐茶嫰閸樺憡绻涢弶鎴炲枠妞ゃ垺锕㈠畷顐﹀Ψ瑜岀粭澶娾攽鎺抽崐鎾绘倿閿旀崘濮虫慨妯垮煐閻撳繘鏌涢埄鍐╃妞わ讣濡囬埀顒€婀辨灙妞ゎ厼鍢查悾鐑芥晲閸ワ附鍕冮梺绋挎湰椤ㄥ懏绂嶉幆顬″綊鏁愰崨顔兼殘闂佸憡娲熺粻鏍蓟閿濆鍊烽柛娆忣樈濡垿姊洪柅鐐茶嫰閸樻悂鏌i幒鐐电暤鐎殿噮鍓熷畷鎺戭潩閿濆棛鍙冩繝娈垮枤閹虫挸煤閻樿纾婚柟鎯х摠婵挳鏌涘☉姗堟敾闁革絾鎮傞弻锝嗘償閵忕姴鏋欓柣鐘冲姉閸犳牠宕洪埀顒併亜閹寸偛顕滄い锕傤棑缁辨挸顓奸崪浣稿壎闂佺娅曢悧鐘诲箠閻樻椿鏁嗛柛鎰亾閽戝绱撻崒娆戝妽闁告劕顭烽獮蹇涙晸閿燂拷28缂傚倸鍊风欢锟犲磻婢舵劦鏁嬬憸鏃堛€佸Δ鍛亜闁惧繐婀遍悡瀣⒑鐟欏嫭鍎楅柛銊ョ-缁牊绻呭▎绯氭繝鐢靛仦濞兼瑩宕ョ€n喗鍤屽Δ锝呭暙缁犵喖鏌ц箛锝呬簼濠殿垱鎸抽獮鏍庨鈧埀顒佹礀閳绘捇鏁撻敓锟�128.00闂傚倷鑳舵灙缂佺粯鍔欏畷鏉库槈濮橆収娼熼梺璺ㄥ櫐閹凤拷