The purpose of this paper presentation is to help students to practice giving talks in front of public at research conferences or other situations, and prepare for the course projects.
The instructor has prepared a list of relevant papers with regard to the course content, most of which are published at the top venues of information retrieval, such as conferences of SIGIR, CIKM, WWW and WSDM, and journals of TOIS and Information Retrieval Journal. Each group of students are required to choose from the list for their presentation. One paper can only be presented by one group of students. Students are required to prepare the slides by themselves (the original authors' slides are not allowed to be used for this presentation).
It is preferred that the students could present the paper on the day when the topic is covered in the lecture, such that the audiences can get different flavors of the topic. The student presentation will be placed at the beginning of the class (unless the student requires to present at the end of the class). Each presentation should be within 15 minutes, and two more minutes only for question and answering purpose (i.e., at most 15+2 minutes in total).
Giving an impressive presentation is an art. Some helpful tips can be found in the resource page.
Both the instructor and other students will grade the presentation (no self-grading). The detailed grading criteria can be found below.
Paper presentation sign up is due in the end of 5th week of the semester (Mar. 5th, 11:59pm).
Presentation Topics
The following is a list of research topics that have attracted most attention in the field of information retrieval in the recent years. Under each topic, the instructor has selected a group of influential papers. Each team is required to choose from the list. But you do want to present a paper outside this list, please contact the instructor before the paper presentation sign up deadline.
- Online learning to rank
- Yue, Yisong, and Thorsten Joachims. "Interactively optimizing information retrieval systems as a dueling bandits problem." In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1201-1208. 2009.
- Schuth, Anne, Harrie Oosterhuis, Shimon Whiteson, and Maarten de Rijke. "Multileave gradient descent for fast online learning to rank." In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 457-466. 2016.
- Oosterhuis, Harrie, and Maarten de Rijke. "Differentiable unbiased online learning to rank." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1293-1302. 2018.
- Wang, Huazheng, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, and Hongning Wang. "Variance reduction in gradient exploration for online learning to rank." In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 835-844. 2019.
- Neural recommendation algorithms
- He, Xiangnan, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. "Neural collaborative filtering." In Proceedings of the 26th international conference on world wide web, pp. 173-182. 2017.
- Li, Jing, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. "Neural attentive session-based recommendation." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419-1428. 2017.
- Fan, Wenqi, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. "Graph neural networks for social recommendation." In The World Wide Web Conference, pp. 417-426. 2019.
- Dacrema, Maurizio Ferrari, Paolo Cremonesi, and Dietmar Jannach. "Are we really making much progress? A worrying analysis of recent neural recommendation approaches." In Proceedings of the 13th ACM Conference on Recommender Systems, pp. 101-109. 2019.
- Neural ranking models
- Dehghani, Mostafa, Hamed Zamani, Aliaksei Severyn, Jaap Kamps, and W. Bruce Croft. "Neural ranking models with weak supervision." In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 65-74. 2017.
- Ahmad, Wasi Uddin, Kai-Wei Chang, and Hongning Wang. "Context attentive document ranking and query suggestion." In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 385-394. 2019.
- Guo, Jiafeng, Yixing Fan, Qingyao Ai, and W. Bruce Croft. "A deep relevance matching model for ad-hoc retrieval." In Proceedings of the 25th ACM international on conference on information and knowledge management, pp. 55-64. 2016.
- Liu, Xiaodong, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang. "Representation learning using multi-task deep neural networks for semantic classification and information retrieval." (2015).
- Sequential recommendation
- Kang, Wang-Cheng, and Julian McAuley. "Self-attentive sequential recommendation." In 2018 IEEE International Conference on Data Mining (ICDM), pp. 197-206. IEEE, 2018.
- Tang, Jiaxi, and Ke Wang. "Personalized top-n sequential recommendation via convolutional sequence embedding." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565-573. 2018.
- Chen, Xu, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. "Sequential recommendation with user memory networks." In Proceedings of the eleventh ACM international conference on web search and data mining, pp. 108-116. 2018.
- Wu, Jibang, Renqin Cai, and Hongning Wang. "Deja vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation." In Proceedings of The Web Conference 2020, pp. 2199-2209. 2020.
- Explainable recommendation
- He, Xiangnan, Tao Chen, Min-Yen Kan, and Xiao Chen. "Trirank: Review-aware explainable recommendation by modeling aspects." In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661-1670. 2015.
- Zhang, Yongfeng, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. "Explicit factor models for explainable recommendation based on phrase-level sentiment analysis." In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 83-92. 2014.
- Wang, Nan, Hongning Wang, Yiling Jia, and Yue Yin. "Explainable recommendation via multi-task learning in opinionated text data." In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 165-174. 2018.
- Xian, Yikun, Zuohui Fu, S. Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. "Reinforcement knowledge graph reasoning for explainable recommendation." In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp. 285-294. 2019.
- Unbiased off-policy learning to rank
- Joachims, Thorsten, Adith Swaminathan, and Tobias Schnabel. "Unbiased learning-to-rank with biased feedback." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 781-789. 2017.
- Wang, Xuanhui, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. "Position bias estimation for unbiased learning to rank in personal search." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 610-618. 2018.
- Ai, Qingyao, Keping Bi, Cheng Luo, Jiafeng Guo, and W. Bruce Croft. "Unbiased learning to rank with unbiased propensity estimation." In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 385-394. 2018.
- Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. "Unbiased lambdamart: an unbiased pairwise learning-to-rank algorithm." In The World Wide Web Conference, pp. 2830-2836. 2019.
- Fairness in ranking/recommendation
- Singh, Ashudeep, and Thorsten Joachims. "Fairness of exposure in rankings." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2219-2228. 2018.
- Biega, Asia J., Krishna P. Gummadi, and Gerhard Weikum. "Equity of attention: Amortizing individual fairness in rankings." In The 41st international acm sigir conference on research & development in information retrieval, pp. 405-414. 2018.
- Beutel, Alex, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt et al. "Fairness in recommendation ranking through pairwise comparisons." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2212-2220. 2019.
- Zehlike, Meike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, and Ricardo Baeza-Yates. "Fa* ir: A fair top-k ranking algorithm." In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1569-1578. 2017.
- Reinforcement learning in result ranking/recommendation
- Zheng, Guanjie, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. "DRN: A deep reinforcement learning framework for news recommendation." In Proceedings of the 2018 World Wide Web Conference, pp. 167-176. 2018.
- Chen, Shi-Yong, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang, and Hai-Hong Tang. "Stabilizing reinforcement learning in dynamic environment with application to online recommendation." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1187-1196. 2018.
- Chen, Minmin, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi. "Top-k off-policy correction for a REINFORCE recommender system." In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 456-464. 2019.
- Feng, Jun, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, and Xiaoyan Zhu. "Learning to collaborate: Multi-scenario ranking via multi-agent reinforcement learning." In Proceedings of the 2018 World Wide Web Conference, pp. 1939-1948. 2018.
Paper Presentation Rubric
| Aspects | Score |
| Slides content was clearly visible and self-explainable | [1-10] |
| Important messages of the paper were properly highlighted | [1-10] |
| Organization and logic of the presentation were easy to follow | [1-15] |
| Explained approaches/methods clearly | [1-15] |
| All students in the team well understood the paper | [0-10] |
| Presenter(s) did not just read off the slides | [0-10] |
| Perfect timing | [0-10] |
| Responded to audience's questions well | [0-10] |
| I have learned something from this presentation and would like to read the paper in future | [0-10] |
Presentation Schedule
| Name | Date | Paper Title |
| Sung Joon Park, Michael Chang, Henry Carscadden | March 30 | Self-attentive sequential recommendation |
| William Helmrath, Ram Karri, David Dimmett, Jihong Min | April 1 | Reinforcement Knowledge Graph Reasoning for Explainable Recommendation |
| Tianyang Chen, Veronique Wang, Zetao Wang, Chenlin Liu | April 1 | Deja vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation |
| Jiajia Liang, Jie Fan, Linyang Du | April 6 | FA*IR: A Fair Top-k Ranking Algorithm |
| Siyu Jian, Meng Hua, Johannes Johnson, Veena Ramesh | April 6 | Neural attentive session-based recommendation |
| Fangzhou Xu, Peiyi Yang, Zice Wei, Shengyuan Piao | April 8 | Differentiable unbiased online learning to rank |
| Jay Rothenberger, Sammy Lahrime, Clara Na, Nikash Sethi | April 8 | Explicit factor models for explainable recommendation based on phrase-level sentiment analysis |
| Farhan Zaman, Ramya Bhaskara, LaDawna McEnhimer, James Perry | April 13 | Representation learning using multi-task deep neural networks for semantic classification and information retrieval |
| Simon Zhu, Yushun Dong, Sihang Jiang, Hanzhi Zhou | April 13 | Personalized top-n sequential recommendation via convolutional sequence embedding |
| Fred Stoney, Ben Barrett, Shuche Wang, Jagroop Sarkaria | April 20 | Multileave gradient descent for fast online learning to rank |
| Theodore Rose, Rajiv Sarvepalli, Daniel Wang | April 22 | Trirank: Review-aware explainable recommendation by modeling aspects |
| Parv Ahuja, Richard Park | April 22 | Are we really making much progress? A worrying analysis of recent neural recommendation approaches |
| Edward Noe, Joseph Banks, Julian Wilson | April 27 | Learning to collaborate: Multi-scenario ranking via multi-agent reinforcement learning |
| Zhiming Fan, Wenbo Pang, Jingyuan Chou | April 27 | Graph neural networks for social recommendation |
| Zac Bilmen, Malcolm Mashig, Deepak Goel | April 29 | Sequential recommendation with user memory networks |
| Ryan Kann, Timothy Han, Pamela Beardsell | April 29 | Interactively optimizing information retrieval systems as a dueling bandits problem. |
| Aldrick Johan, Wei Wang, Matthew Bacon, Keshav Ailaney | May 4 | DRN: A deep reinforcement learning framework for news recommendation |
| William Wong, Christopher Raley, Amar Kulkarni, Rohan Nair | May 4 | Explainable recommendation via multi-task learning in opinionated text data |
Peer-evaluation website
Please use this page for peer evaluation.