Publications

[Google Scholar] [DBLP] [Selected Publications]

Preprints

  1. Anat Hashavit, Hongning Wang, Tamar Stern, Sarit Kraus. Not Just Skipping. Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health Search. arXiv:2207.04445, 2022. (PDF)
  2. Chuanhao Li, Huazheng Wang, Mengdi Wang and Hongning Wang. Communication Efficient Distributed Learning for Kernelized Contextual Bandits. arXiv:2206.04835, 2022. (PDF)
  3. Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long and Lingfei Wu. Meta Policy Learning for Cold-Start Conversational Recommendation. arXiv:2205.11788, 2022. (PDF)
  4. Chuanhao Li and Hongning Wang. Communication Efficient Federated Learning for Generalized Linear Bandits. arXiv:2202.01087, 2022. (PDF)
  5. Huazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu and Hongning Wang. Incentivizing Exploration in Linear Bandits under Information Gap. arXiv:2104.03860, 2021. (PDF)
  6. Yiling Jia and Hongning Wang. Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank. arXiv:2111.00735, 2021. (PDF)

2022

  1. Huazheng Wang, David Zhao and Hongning Wang. Dynamic Global Sensitivity for Differentially Private Contextual Bandits. The ACM Conference on Recommender Systems (RecSys'2022), 2022. (To Appear)
  2. Lu Lin, Ethan Blaser and Hongning Wang. Graph Structural Attack by Perturbing Spectral Distance. SIGKDD Conference On Knowledge Discovery And Data Mining (KDD'2022), 2022. (PDF)
  3. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu. Learning from a Learning User for Optimal Recommendations. The Thirty-ninth International Conference on Machine Learning (ICML'2022), 2022. (PDF)
  4. Huazheng Wang, Haifeng Xu and Hongning Wang. When Are Linear Stochastic Bandits Attackable?. The Thirty-ninth International Conference on Machine Learning (ICML'2022), 2022. (PDF)
  5. Nan Wang, Hongning Wang, Maryam Karimzadehgan, Branislav Kveton and Craig Boutilier. IMO3: Interactive Multi-Objective Off-Policy Optimization. The 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022). (PDF)
  6. Yiling Jia and Hongning Wang. Scalable Exploration for Online Learning to Rank with Perturbed Click Feedback. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2022). (PDF)
  7. Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu and Hongning Wang. Learning Neural Contextual Bandits through Perturbed Rewards. The Tenth International Conference on Learning Representations (ICLR'2022). (PDF)
  8. Chuanhao Li and Hongning Wang. Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. The 25th International Conference on Artificial Intelligence and Statistics (AISTATS'2022). (PDF)
  9. Aobo Yang, Nan Wang, Renqin Cai, Hongbo Deng and Hongning Wang. Comparative Explanations of Recommendations. The ACM Web Conference 2022 (WWW'2022). (PDF)
  10. Nan Wang, Lu Lin, Jundong Li and Hongning Wang. Unbiased Graph Embedding with Biased Graph Observations. The ACM Web Conference 2022 (WWW'2022). (PDF)
  11. Peng Wang, Renqin Cai and Hongning Wang. Graph Based Extractive Explainer for Recommendations. The ACM Web Conference 2022 (WWW'2022). (PDF, code)
  12. Yiling Jia and Hongning Wang. Learning Neural Ranking Models Online from Implicit User Feedback. The ACM Web Conference 2022 (WWW'2022). (PDF, code)
  13. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu. Learning the Optimal Recommendation from Explorative Users. The 36th AAAI Conference on Artificial Intelligence (AAAI'2022), 2022. (PDF)
  14. Lu Lin, Ethan Blaser and Hongning Wang. Graph Embedding with Hierarchical Attentive Membership. The 15th International Conference on Web Search and Data Mining (WSDM'2022), 2022. (PDF)

2021

  1. Andrew Villca-rocha, Max Zheng, Chengzhu Duan and Hongning Wang. Towards Semantic Search in Building Sensor Data. The 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys'2021), p164-167, 2021. (PDF)
  2. Zhendong Chu and Hongning Wang. Improve Learning from Crowds via Generative Augmentation. The 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD'2021), p167-175, 2021. (PDF, code)
  3. Huazheng Wang, Yiling Jia and Hongning Wang. Interactive Information Retrieval with Bandit Feedback. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2021), tutorial, p2658-2661, 2021. (website)
  4. Chuanhao Li, Qingyun Wu and Hongning Wang. When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2021), p1410–1419, 2021. (PDF)
  5. Renqin Cai, Hongning Wang, Jibang Wu, Chong Wang and Aidan San. Category-aware Collaborative Sequential Recommendation. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2021), p388-397, 2021. (PDF)
  6. Anat Hashavit, Sarit Kraus, Tamar Stern, Raz Lin and Hongning Wang. Understanding and Mitigating Bias in Online Health Search. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'2021), p265-274, 2021. (PDF)
  7. Chuanhao Li, Qingyun Wu and Hongning Wang. Unifying Clustered and Non-stationary Bandits. The 24th International Conference on Artificial Intelligence and Statistics (AISTATS'2021), p1063-1071, 2021. (PDF)
  8. Yiling Jia, Huazheng Wang, Stephen Guo and Hongning Wang. PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer. The Web Conference 2021 (WWW'2021), p146-157, 2021. Nominated for the Best Paper Award (PDF, code)
  9. Zhendong Chu, Jing Ma and Hongning Wang. Learning from Crowds by Modeling Common Confusions. The 35th AAAI Conference on Artificial Intelligence (AAAI'2021), p5832-5840, 2021. (PDF, code)
  10. Nan Wang, Zhen Qin, Xuanhui Wang and Hongning Wang. Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction. The 14th ACM International WSDM Conference (WSDM'2021), p481–489, 2021. (PDF)
  11. Aobo Yang, Nan Wang, Hongbo Deng and Hongning Wang. Explanation as a Defense of Recommendation. The 14th ACM International WSDM Conference (WSDM'2021), p1029–1037, 2021. (PDF, code)

2020

  1. Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan and Hongning Wang. Global and Local Differential Privacy for Collaborative Bandits. The 14th ACM Conference on Recommender Systems (RecSys'20), p150–159, 2020. (PDF)
  2. Huazheng Wang, Qingyun Wu and Hongning Wang. Learning by Exploration: New Challenges in Real-World Environments. The 26th ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD 2020), p3575–3576, tutorial 2020. (PDF, website)
  3. Lu Lin and Hongning Wang. Graph Attention Networks over Edge Content-Based Channels. The 26th ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD 2020), p1819–1827, 2020. (PDF)
  4. Nan Wang and Hongning Wang. Directional Multivariate Ranking. The 26th ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD 2020), p1819–1827, 2020. (PDF)
  5. Jibang Wu, Renqin Cai and Hongning Wang. Déjà vu: The Contextualized Temporal Attention Mechanism for Sequential Recommendation. The Web Conference 2020 (WWW 2020). (PDF)
  6. Jing Ma, Dezhi Hong and Hongning Wang. Selective Sampling for Sensor Type Classification in Buildings. 19th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 2020). (PDF)
  7. Shuheng Li, Dezhi Hong and Hongning Wang. Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI'2020). (PDF)
  8. Lin Gong, Lu Lin, Weihao Song and Hongning Wang. JNET: Learning User Representations via Joint Network Embedding and Topic Embedding. The 13th ACM International Conference on Web Search and Data Mining (WSDM 2020). (PDF)

2019

  1. Xueying Bai, Jian Guan and Hongning Wang. Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation. The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 2019. (PDF, code)
  2. Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao and Hongning Wang. Adversarial Domain Adaptation for Machine Reading Comprehension. 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP 2019). (PDF)
  3. Yiling Jia, Nipun Batra, Kamin Whitehouse and Hongning Wang. Active Collaborative Sensing for Energy Breakdown. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019). (PDF, code)
  4. Zhendong Chu, Renqin Cai and Hongning Wang. Accounting for Temporal Dynamics in Document Streams. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019). (PDF)
  5. Dezhi Hong, Renqin Cai, Hongning Wang and Kamin Whitehouse. Learning from Correlated Events for Equipment Relation Inference in Buildings. The 6th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation (BuildSys 2019). (PDF)
  6. Lu Lin, Zheng Luo, Dezhi Hong and Hongning Wang. Sequential Learning with Active Partial Labeling for Building Metadata. The 6th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation (BuildSys 2019). (PDF)
  7. Qingyun Wu, Zhige Li, Huazheng Wang, Wei Chen and Hongning Wang. Factorization Bandits for Online Influence Maximization. The 25th ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD 2019), p636-646, 2019. (PDF, code)
  8. Yiyi Tao, Yiling Jia, Nan Wang and Hongning Wang. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), p295-304, 2019. (PDFcode)
  9. Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu and Hongning Wang. Variance Reduction in Gradient Exploration for Online Learning to Rank. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), p835-844, 2019. Best Paper Award (PDFcode)
  10. Wasi Uddin Ahmad, Kai-Wei Chang and Hongning Wang. Context Attentive Document Ranking and Query Suggestion. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), p385-394, 2019. (PDF, code)
  11. Qingyun Wu, Huazheng Wang, Yanen Li and Hongning Wang. Dynamic Ensemble of Contextual Bandits to Satisfy Users' Changing Interests. The Web Conference 2019 (WWW 2019), p2080-2090, 2019. (PDF, code)
  12. Yiling Jia, Nipun Batra, Kamin Whitehouse and Hongning Wang. A Tree-Structured Neural Network Model for Household Energy Breakdown. The Web Conference 2019 (WWW 2019), p2872-2878, 2019. (PDF, code)
  13. Lu Lin, Lin Gong and Hongning Wang. Learning Personalized Topical Compositions with Item Response Theory. The 12th ACM International Conference on Web Search and Data Mining (WSDM 2019), p609-617, 2019. (PDF)

2018

  1. Qi Yi, Qingyun Wu, Hongning Wang, Jie Tang and Maosong Sun. Bandit Learning with Implicit Feedback. The 32nd Conference on Neural Information Processing Systems (NIPS 2018), p7287-7297, 2018. (PDF)
  2. Jason Koh, Dezhi Hong, Rajesh Gupta, Kamin Whitehouse, Hongning Wang, and Yuvraj Agarwal. Plaster: An Integration, Benchmark, and Development Framework for Metadata Normalization Methods. 5th ACM International Conference on Systems for Built Environments (BuildSys 2018), p1-10, 2018. (PDF)
  3. Renqin Cai, Xueying Bai, Yuling Shi, Zhenrui Wang, Parikshit Sondhi and Hongning Wang. Modeling Sequential Online Interactive Behaviors with Temporal Point Process. The 27th International Conference on Information and Knowledge Management (CIKM 2018), p873-882, 2018. (PDF)
  4. Lin Gong and Hongning Wang. When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data. The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), p1455-1464, 2018. (PDF)
  5. Elaheh Sadredini, Deyuan Guo, Chunkun Bo, Reza Rahimi, Hongning Wang and Kevin Skadron. A Scalable Solution for Rule-Based Part-of-Speech Tagging on Novel Hardware Accelerators. The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), p665-674, 2018. (PDF)
  6. Nan Wang, Yiling Jia, Yue Yin and Hongning Wang. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), p165-174, 2018. (PDF, code)
  7. Wasi Ahmad, Kai-Wei Chang and Hongning Wang. Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), p285-294, 2018. (PDF)
  8. Qingyun Wu, Naveen Iyer and Hongning Wang. Learning Contextual Bandits in a Non-stationary Environment. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), p495-504, 2018. (PDF, code)
  9. Huazheng Wang, Ramsey Langley, Sonwoo Kim, Eric McCord-Snook and Hongning Wang. Efficient Exploration of Gradient Space for Online Learning to Rank. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), p145-154, 2018. (PDF, code)
  10. Puxuan Yu, Wasi Ahmad and Hongning Wang. Hide-n-Seek: An Intent-aware Privacy Protection Plugin for Personalized Web Search. The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018), demo track, p1333-1336, 2018. (PDF)
  11. Wasi Uddin Ahmad, Kai-Wei Chang, and Hongning Wang. Multi-Task Learning for Document Ranking and Query Suggestion. Sixth International Conference on Learning Representations (ICLR 2018), 2018. (PDF, code)
  12. Nipun Batra, Yiling Jia, Hongning Wang, and Kamin Whitehouse. Transferring Decomposed Tensors for Scalable Energy Breakdown across Regions. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI'2018), p740-747, 2018. (PDF, code)

2017

  1. Diheng Zhang, Yuling Shi, Hongning Wang, and Bethany A. Teachman. Predictors of Attrition in a Public Online Interpretation Training Program for Anxiety. 51st Annual Convention of Association for Behavioral and Cognitive Therapies (poster paper), 2017. (Poster)
  2. Qingyun Wu, Hongning Wang, Liangjie Hong and Yue Shi. Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems. The 26th International Conference on Information and Knowledge Management (CIKM 2017), p1927-1936, 2017. (PDF, code)
  3. Yuling Shi, Zhiyong Peng and Hongning Wang. Modeling Student Learning Styles in MOOCs. The 26th International Conference on Information and Knowledge Management (CIKM 2017), p979-988, 2017. (PDF)
  4. Yue Wang, Hongning Wang and Hui Fang. Extracting User-Reported Mobile Application Defects from Online Reviews. 2017 IEEE International Conference on Data Mining Workshops (ICDMW), SENTIRE (2017), p422-429, 2017. (PDF)
  5. Hongning Wang, Rui Li, Milad Shokouhi, Hang Li, and Yi Chang. Search, Mining, and Their Applications on Mobile Devices: Introduction to the Special Issue. ACM Transactions on Information Systems (TOIS), special issue, 2017. (PDF)
  6. Renqin Cai, Chi Wang and Hongning Wang. Accounting for the Correspondence in Commented Data. The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), p365-374, 2017. (PDF)
  7. Derek Wu and Hongning Wang. ReviewMiner: An Aspect-based Review Analytics System. The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Demo Track (SIGIR 2017), p1285-1288, 2017. (PDF, link)
  8. Asif Salekin, Hongning Wang, Kristine Williams and John Stankovic. DAVE: Detecting Agitated Vocal Events. the IEEE 2nd International Conference on Connected Health: Applications, Systems and Engineering Technologies (IEEE CHASE 2017), p157-166, 2017. (PDF)
  9. Lin Gong, Benjamin Haines and Hongning Wang. Clustered Model Adaptation for Personalized Sentiment Analysis. The 26th International World Wide Web Conference (WWW 2017), p937-946, 2017. (PDF, Slides, code)
  10. Sarah Masud Preum, Abu Sayeed Mondol, Meiyi Ma, Hongning Wang and John A. Stankovic. Preclude: Conflict Detection in Textual Health Advice. The 15th IEEE International Conference on Pervasive Computing and Communications (PerCom 2017), p286-297. (PDF)
  11. Huazheng Wang, Qingyun Wu and Hongning Wang. Factorization Bandits for Interactive Recommendation. The Thirty-First AAAI Conference on Artificial Intelligence (AAAI'2017). (PDF, Supplement, code, code)
  12. Nipun Batra, Hongning Wang, Amarjeet Singh and Kamin Whitehouse. Matrix Factorisation for Scalable Energy Breakdown. The Thirty-First AAAI Conference on Artificial Intelligence (AAAI'2017). (PDF, code)

2016

  1. Huazheng Wang, Qingyun Wu and Hongning Wang. Learning Hidden Features for Contextual Bandits. The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), p1633-1642, 2016. (PDF, code)
  2. Lin Gong, Mohammad Al Boni and Hongning Wang. Modeling Social Norms Evolution for Personalized Sentiment Classification. The 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), p855-865, 2016. (PDF, code)
  3. Qingyun Wu, Huazheng Wang, Quanquan Gu and Hongning Wang. Contextual Bandits in A Collaborative Environment. The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), p529-538, 2016. (PDF, Slides, code)
  4. Wasi Ahmad, Md Masudur Rahman and Hongning Wang. Topic Model based Privacy Protection in Personalized Web Search. The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016), p1025-1028, 2016. (PDF)
  5. Shengwen Peng, Ronghui You, Hongning Wang, Chengxiang Zhai, Hiroshi Mamitsuka and Shanfeng Zhu. DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing. The 23th Intelligent Systems for Molecular Biology (ISMB 2016), p70-79, 2016. (PDF)
  6. Md Mustafizur Rahman and Hongning Wang. Hidden Topic Sentiment Model. The 25th International World-Wide Web Conference (WWW 2016), p155-165, 2016. (PDF, Slides)

2015

  1. Peilin Yang, Hongning Wang, Hui Fang and Deng Cai. Opinions matter: a general approach to user profile modeling for contextual suggestion. Information Retrieval Journal, pp 1-25, DOI 10.1007/s10791-015-9278-7. (Link)
  2. Dezhi Hong, Hongning Wang, Jorge Ortiz and Kamin Whitehouse. The Building Adapter: Towards Quickly Applying Building Analytics at Scale. ACM BuildSys 2015, p123-132, 2015. (Best Paper Candidate) (PDF, slides)
  3. Asif Salekin, Hongning Wang and John Stankovic. KinVocal: Detecting Agitated Vocal Events. The 13th ACM Conference on Embedded Networked Sensor Systems (SenSys 2015), p459-460, 2015. (PDF)
  4. Dezhi Hong, Hongning Wang and Kamin Whitehouse. Clustering-based Active Learning on Sensor Type Classification in Buildings. The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), p363-372, 2015. (PDF, slides)
  5. Mohammad Al Boni, Keira Qi Zhou, Hongning Wang and Matthew S. Gerber. Model Adaptation for Personalized Opinion Analysis. The 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015), p769-774, 2015. (PDF)

2014

  1. Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan and Ryen White. Modeling Action-level Satisfaction for Search Task Satisfaction Prediction. The 37th Annual ACM SIGIR Conference (SIGIR 2014), p123-132, 2014. (PDF, slides)
  2. Yanen Li, Anlei Dong, Hongning Wang, Hongbo Deng, Yi Chang and ChengXiang Zhai. A Two-dimensional Click Model for Query Auto-completion. The 37th Annual ACM SIGIR Conference (SIGIR 2014), p455-464, 2014. (PDF, slides)
  3. Hongning Wang, Anlei Dong and Yi Chang. Joint Learning Approach from Clickthroughs. book chapter in Bo Long and Yi Chang (eds), Relevance Ranking for Vertical Search Engines, Morgan Kaufmann Publisher, 2014, p10-26.
  4. Hongning Wang, ChengXiang Zhai, Feng Liang, Anlei Dong and Yi Chang. User Modeling in Search Logs via A Nonparametric Bayesian Approach. The 7th ACM Web Search and Data Mining Conference (WSDM 2014), p203-212, 2014. (PDF, slides)
  5. Yang Song, Hongning Wang and Xiaodong He. Adapting Deep RankNet for Personalized Search. The 7th ACM Web Search and Data Mining Conference (WSDM 2014), p83-92, 2014. (PDF, slides)

2013

  1. Hongning Wang, Xiaodong He, Ming-Wei Chang, Yang Song, Ryen White and Wei Chu. Personalized Ranking Model Adaptation for Web Search. The 36th Annual ACM SIGIR Conference (SIGIR 2013), p323-332, 2013. (PDF, slides)
  2. Hongning Wang, ChengXiang Zhai, Anlei Dong and Yi Chang. Content-Aware Click Modeling. The 23rd International World-Wide Web Conference (WWW 2013), p1365-1376, 2013. (PDF, slides, codes)
  3. Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ryen White and Wei Chu. Learning to Extract Cross-Session Search Tasks. The 23rd International World-Wide Web Conference (WWW 2013), p1353-1364, 2013. (PDF, slides)
  4. Yang Song, Hao Ma, Hongning Wang and Kuansan Wang. Exploring and Exploiting User Search Behavior on Mobile and Tablet Devices to Improve Search Relevance. The 23rd International World-Wide Web Conference (WWW 2013), p1201-1212, 2013. (PDF)
  5. Ryen White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song and Hongning Wang. Enhancing Personalized Search by Mining and Modeling Task Behavior. The 23rd International World-Wide Web Conference (WWW 2013), p1411-1420, 2013. (PDF)
  6. Chi Wang, Hongning Wang, Jialu Liu, Ming Ji, Lu Su, Yuguo Chen, Jiawei Han. On the Detectability of Node Grouping in Networks. SIAM International Conference on Data Mining (SDM 2013), p713-721, 2013. (PDF)
  7. Hongbo Deng, Jiawei Han, Hao Li, Heng Ji, Hongning Wang and Yue Lu. Exploring and Inferring User-User Pseudo-Friendship for Sentiment Analysis with Heterogeneous Networks. SIAM International Conference on Data Mining (SDM 2013), p378-386, 2013. (PDF)
  8. Mianwei Zhou, Hongning Wang and Kevin Chen-Chuan Chang. Learning to Rank from Distant Supervision: Exploiting Noisy Redundancy for Relational Entity Search. The 29th IEEE International Conference on Data Engineering (ICDE 2013), p829-840, 2013. (PDF)

2012

  1. Yue Lu, Hongning Wang, ChengXiang Zhai and Dan Roth. Unsupervised Discovery of Opposing Opinion Networks From Forum Discussions. The 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), p1642-1646, 2012. (PDF)
  2. Hongning Wang, Anlei Dong, Lihong Li, Yi Chang and Evgeniy Gabrilovich. Joint Relevance and Freshness Learning From Clickthroughs for News Search. The 2012 World Wide Web Conference (WWW 2012), p579-588, 2012. (PDF, slides)

2011

  1. Hongning Wang, Yue Lu and ChengXiang Zhai. Latent Aspect Rating Analysis without Aspect Keyword Supervision. The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2011), P618-626, 2011. (PDF, slides, Demo)
  2. Hongning Wang, Chi Wang, ChengXiang Zhai and Jiawei Han. Learning Online Discussion Structures by Conditional Random Fields. The 34th Annual International ACM SIGIR Conference (SIGIR 2011), P435-444, 2011. (PDF, slides, codes)
  3. Hongning Wang, Duo Zhang and ChengXiang Zhai. Structural Topic Model for Latent Topical Structure Analysis. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL HTL 2011), P1526-1535, 2011. (PDF, codes)

2010

  1. Yue Lu, Huizhong Duan, Hongning Wang and ChengXiang Zhai. Exploiting Structured Ontology to Organize Scattered Online Opinions. The 23rd International Conference on Computational Linguistics (COLING 2010) P734--742, 2010. (PDF)
  2. Hongning Wang, Yue Lu and Chengxiang Zhai. Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), p783-792, 2010. (PDF, slides, Demo, codes)

Before 2010

  1. Hongning Wang, Minlie Huang and Xiaoyan Zhu. Extract Interaction Detection Methods from the Biological Literature. BMC Bioinformatics 2009, 10(Suppl 1):S55. (PDF)
  2. Hongning Wang, Minlie Huang and Xiaoyan Zhu. A Generative Probabilistic Model for Multi-Label Classification. In Proceedings of the IEEE 8th International Conference on Data Mining (IEEE ICDM 2008), p628-637, 2008. (PDF)
  3. Hongning Wang, Minlie Huang, Shilin Ding and Xiaoyan Zhu. Exploiting and Integrating Rich Features for Biological Literature Classification. BMC Bioinformatics. 2008; 9(Suppl 3): S4. (PDF)
  4. Minlie Huang, Shilin Ding, Hongning Wang and Xiaoyan Zhu. Mining Physical Protein-protein Interactions from the Literature. Genome Biology 2008, 9(Suppl 2):S12. (PDF)