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


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.


If you are in the academia job market: the School 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!


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      


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)


[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.