Hongning Wang

News

• December 15, 2023

  Thrilled to receive the Karen Spärck Jones (KSJ) Award 2023!

• December 2, 2022

  Very glad to receive the Copenhaver Associate Professor appointment from the School of Engineering!

• March 20, 2022

  We have reorganized our group's algorithms and released most of them through this GitHub site. You are highly welcomed to try them out and provide us feedback!

• January 1, 2022

  Interested in multi-arm bandits? You can now play with such algorithms here.

• October 1, 2021

  Our research in "Moving offline learning to rank online, from theory to practice" has been funded by NSF IIS program. More details about this award can be found here.

• August 1, 2021

  I will be visiting the Department of Computer Science and Technology at Tsinghua University in the academic year 2021-22 for my sabbatical leave.

• December 6, 2020

  I am offering CS4780 Information Retrieval in the spring semester.

• October 1, 2020

  Our research in "Towards Explainable Personalization" has been funded by NSF IIS program. More details about this award can be found here.

• August 27, 2020

  I am offering CS6501 Reinforcement Learning in the fall semester.

• August 23, 2020

  We are giving a tutorial on Learning by Exploration at KDD'2020.

• July 23, 2019

  I am offering CS4780/6501 Information Retrieval in the spring semester.

• July 23, 2019

  Our paper "Variance Reduction in Gradient Exploration for Online Learning to Rank" received the Best Paper Award at SIGIR'2019!

• May 16, 2019

  I gave an invitated talk about "Join Network Embedding with Topic Embedding for User Representation Learning" at LinkedIn.

• May 15, 2019

  I gave an invitated talk about "Learning Contextual Bandits in a Non-Stationary Environment" at Pinterest.

• Feburary 15, 2019

  I gave an invitated talk about "Context Attentive Document Ranking and Query Suggestion in Search Tasks" at the Task Intelligence Workshop, WSDM'2019, Melbourne Australia.

• Feburary 12, 2019

  I was selected for the WSDM 2019 Outstanding Senior Program Committee Award.

• September 15, 2018

  Our research in "Learning and Improving Alzheimer's Patient-Caregiver Relationships via Smart Healthcare Technology" has been funded by NSF SCH program. More details about this award can be found here.

• Janurary 24, 2018

  Thanks Nvidia for the generious support of a Titan XP GPU!

• December 24, 2017

  Our research of "The Building Adapter: Automatic Mapping of Commercial Buildings for Scalable Building Analytics" has been funded by the U.S. Department of Energy. More details about this award can be found here.

• September 1, 2017

  Our research of "Cyber Physical Mappings - Empower Building Analytics at Scale" has been funded by NSF IIS program. More details about this award can be found here.

• July 18, 2017

  I gave an invitated talk about "Contextual Bandits in a Collaborative Environment" at the Department of Computer Science and Technology, Tsinghua University, Beijing China.

• June 15, 2017

  We have been awarded by the Yelp Dataset Challenge Round Eight!

• January 1, 2017

  Our group homepage is now available at HCDM@UVa.

• September 16, 2016

  Our research of "Collaborative Sensing: An Approach for Immediately Scalable Sensing in Buildings" has been funded by NSF CPS program. More details about this award can be found here.

• August 1, 2016

  Our research of "Collaborative Learning with Incomplete and Noisy Knowledge" has been funded by NSF III program. More details about this award can be found here.

• March 5, 2016

  I gave an invitated talk about "Collaborative Online Learning" at Yahoo Search Science Team.

• December 30, 2015

  I have received the NSF Faculty Early Career Development Program (CAREER) Award. More details about this award can be found here and our project website is here.

• December 21, 2015

  Our proposal of "Call for Special Issue on Search, Mining and their Applications on Mobile Devices" has been accepted by the ACM Transactions on Information Systems (TOIS). Call for papers will be announced shortly.

• March 20, 2015

  I gave an invitated talk about "Human-centric big data mining" at Center for Embedded Systems for Critical Applications (CESCA) in the Department of Electrical and Computer Engineering at Virginia Tech.

• Janurary 29, 2015

  I gave an invitated talk about "Human-centric big data mining" in Quantitative Psychology Group in the Department of Psychology at UVa.

• Janurary 12, 2015

  My research is reported on UVa Today.

• December 22, 2014

  The course website for CS6501 Text Mining has been deployed.

• December 9, 2014

  I was selected for the WSDM 2015 Outstanding Reviewer Award.

• December 1, 2014

  I have position opennings for Ph.D. students for Fall 2015. If you are self-motivated and want to pursue a Ph.D. degree in the areas of data mining and information retrieval, please contact me. More information about the graduate admission at CS@UVa can be found here. Please note the application deadline is December 15th, 2014.

• November 10, 2014

  I am going to offer CS6501: Text Mining next Spring semester.

• August 26, 2014

  I am offering CS6501: Information Retrieval this Fall semester.

• August 25, 2014

  I joined CS@UVa as an assistant professor.

Award and Honor

• 2023 Dec, Karen Spärck Jones (KSJ) Award;
• 2022 Dec, the Copenhaver Associate Professor appointment;
• 2022 Dec, NeurIPS 2022 Top Reviewer;
• 2020 Mar, Yelp Dataset Challenge Round Thirteen Winner;
• 2020 Feb, Google Faculty Research Award;
• 2020 Feb, finalist of Microsoft Research Faculty Fellowship;
• 2019 Jul, SIGIR 2019 Best Paper Award;
• 2019 Feb, WSDM 2019 Outstanding Senior Program Committee award;
• 2017 Jul, SIGIR 2017 Outstanding Reviewer award;
• 2017 Jun, Yelp Dataset Challenge Round Eight Winner;
• 2015 Dec, NSF Faculty Early Career Development Program (CAREER) Award, Project Website;
• 2014 Oct, Yahoo 2014 Academic Career Enhancement award;
• 2014 Jul, U.Va. Excellence in Diversity Fellowship;

Invited Talks

• Next++ Workshop, Tsinghua Singapore AI summit, May 30, 2024. "Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?".
• Georgetown Computer Science Colloquium, Feburary 13, 2024. "How Bad is Top-K Recommendation under Competing Content Creators?".
• Illinois Computer Science Speaker Series Lecture, Feburary 7, 2024. "How Bad is Top-K Recommendation under Competing Content Creators?".
• Glasgow Information Retrieval group seminar talk, November 14, 2022. "Rethink conversational recommendations and beyond".
• Workshop on Action, Task and User Journey Modeling at Google, October 5, 2022. "Learning from learning users: a new perspective for interactive recommendation".
• Instacart Machine Learning Seminar, September 28, 2022. "A New Perspective for Interactive Recommendation".
• AI/ML Seminar, Meta AI, Jun 10, 2022. "Learning from a learning user: a new perspective for interactive recommendation".
• Microsoft Research Asia, Apr 29, 2022. "Learning from a learning user: a new perspective for interactive recommendation".
• AI Lecture Series, School of Artificial Intelligence, Jilin University, Dec 20, 2021. "Taming Latent Factor Models for Explainability".
• AI Seminar, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Oct 26, 2021. "Moving offline learning to rank online, from theory to practice".
• Computer Science Colloquium, Department of Computer Science and Technology, Tsinghua University, Sept 21, 2021. "Moving offline learning to rank online, from theory to practice".
• Cornell AI Seminar, Mar 26, 2021. "Moving offline learning to rank online, from theory to practice".
• booking.com, Nov 9, 2020. "Learning Contextual Bandits in a Non-Stationary Environment".
• University of Delaware, Department of Electrical and Computer Engineering, Nov 6, 2019. "Taming Latent Factor Models for Explainability.".
• LinkedIn, May 16, 2019. "Join Network Embedding with Topic Embedding for User Representation Learning".
• Pinterest, May 15, 2019. "Learning Contextual Bandits in a Non-Stationary Environment".
• Task Intelligence Workshop, the 12th ACM International Conference on Web Search and Data Mining (WSDM'2019), Feburary 15, 2019. "Context Attentive Document Ranking and Query Suggestion in Search Tasks".
• University of Montreal, Laboratoire RALI, December 6, 2018. "Learning Contextual Bandits in a Non-Stationary Environment".
• Columbia University, Department of Statistics, October 8, 2018. "Learning Contextual Bandits in a Non-Stationary Environment".
• University of Southern California, Information Sciences Institute, Feburary 9, 2018. "Contextual Bandits in a Collaborative Environment".
• Snap Research Group, Feburary 7, 2018. "Contextual Bandits in a Collaborative Environment".
• Amazon Applied Research Group, October 2, 2017. "Contextual Bandits in a Collaborative Environment".
• Department of Computer Science, Tsinghua University, July 18, 2017. "Contextual Bandits in a Collaborative Environment".
• Yahoo Search Science team, March 5, 2016. "Collaborative online learning".
• Center for Embedded Systems for Critical Applications (CESCA) in the Department of Electrical and Computer Engineering at Virginia Tech, March 20, 2015. "Human-centric big data mining".
• Quantitative Psychology Group in the Department of Psychology at UVa, Janurary 2015. "Human-centric big data mining".
• University of Delaware, Department of Electrical and Computer Engineering, October 2014. "A Task-based Framework for User Behavior Modeling and Search Personalization".
• The 4th workshop on Web Search Click Data (WSCD'2014), Feburary 2014. Keynote speech on "A Search-task-based Framework for Modeling User Search Behaviors" (slides).
• Search science and anti-abuse science group, Yahoo! Labs, October 2013. Invited talk on "A Task-Based Framework for Search Log Mining and Personalization".

Academic Activities

• Program committee member:
  • International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR): 2023 (SPC), 2021, 2020, 2019, 2018, 2017, 2016, 2015
  • International ACM KDD Conference on Knowledge Discovery and Data Mining (KDD): 2023 (AC), 2022 (SPC), 2021, 2020, 2019, 2018, 2017, 2016, 2015
  • The World Wide Web Conference (WWW): 2023 (SPC), 2022, 2020 (SPC), 2019, 2018, 2016, 2015
  • ACM International Conference on Web Search and Data Mining (WSDM): 2023 (SPC), 2022 (SPC), 2021, 2019 (SPC), 2018 (SPC), 2017, 2016, 2015
  • Conference on Neural Information Processing Systems (NeurIPS): 2024 (AC), 2022, 2021, 2020, 2019
  • Annual Meeting of the Association for Computational Linguistics (ACL): 2023 (AC), 2019, 2018, 2017, 2015
  • ACM International Conference on Information and Knowledge Management (CIKM): 2022 (SPC), 2021 (SPC), 2020 (SPC), 2017, 2015, 2014
  • International Conference on Machine Learning (ICML): 2022, 2021, 2020, 2014, 2013, 2012
  • International Conference on Learning Representations (ICLR): 2023, 2022, 2021, 2020
  • Association for the Advancement of Artificial Intelligence (AAAI): 2025(AC), 2024 (AC), 2023 (AC), 2022 (AC), 2021 (SPC), 2019, 2018, 2017
• Journal associate editor: Frontiers in Big Data, ACM Transactions on Intelligent Systems and Technology (TIST)
• Journal reviewer: JMLR, TKDE, TOIS, TPAMI, TOIS, Neurocomputing, BMC Bioinformatics, Information Processing & Management, IJAIT, Neural Processing Letters, World Wide Web Journal, International Journal of Machine Learning and Cybernetics.
• Public services: SIGIR 2024 General Co-Chair, KDD 2022 Workshop Co-Chair, KDD 2020 Student Sponsorship Co-Chair, SIGIR 2018 Student Sponsorship Chair, WSDM 2018 Demo Track Chair, CIKM 2016 Publicity chair, AIRS 2016 area chair for IR Models and Theories, NLPCC 2015 area chair for Search and Advertisement.

Selected Publications

1. Haozhe Ji, Cheng Lu, Yilin Niu, Pei Ke, Hongning Wang, Jun Zhu, Jie Tang and Minlie Huang. Towards Efficient and Exact Optimization of Language Model Alignment. The Forty-first International Conference on Machine Learning (ICML'2024), 2024. (PDF)
2. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu. Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict? The Forty-first International Conference on Machine Learning (ICML'2024), 2024. (PDF)
3. Fan Yao, Yiming Liao, Mingzhe Wu, Chuanhao Li, Yan Zhu, James Yang, Qifan Wang, Haifeng Xu and Hongning Wang. User Welfare Optimization in Recommender Systems with Competing Content Creators 30TH ACM SIGKDD Conference On Knowledge Discovery And Data Mining (KDD'2024), 2024. (PDF)
4. Zhepei Wei, Chuanhao Li, Tianze Ren, Haifeng Xu and Hongning Wang. Incentivized Truthful Communication for Federated Bandits. The Eleventh International Conference on Learning Representations (ICLR'2024), 2024. (PDF)
5. Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang and Haifeng Xu. How Bad is Top-K Recommendation under Competing Content Creators? The Fortieth International Conference on Machine Learning (ICML'2023), Oral presentation, 2023. (PDF)
6. Chuanhao Li and Hongning Wang. Communication Efficient Federated Learning for Generalized Linear Bandits. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'2022), 2022. (PDF)
7. 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. Nominated for the Best Paper Award (PDF, code)

Open Implementations

• BanditLib: It is a python library for a rich collection of bandit algorithms, ranging from context-free multi-armed bandits to contextual linear bandits, neural bandits, collaborative bandits, and non-stationary bandits. We also include various testing environments, from synthetic data based simulations to large-scale real-world data based evaluations.
• Online Learning to Rank: It is a python library for our group’s developed online learning to rank algorithms. It is based on a public git repo for simulated online learning to rank environment.
• xRec: It is a python library for our group’s developed explainable recommendation algorithms. It covers algorithms for explaining both latent factor based and neural network based recommendation models.