Learning algorithms mitigate impact of fraud on product rankings
Insights on how to design methods for uncertain environments to guarantee robustness in the face of manipulation.
Faculty
Negin Golrezaei is an Associate Professor of Operations Management at the MIT Sloan School of Management.
Her current research interests are in the area of machine learning, statistical learning theory, mechanism design, and optimization algorithms with applications to revenue management, pricing, and online markets. Before joining MIT, Negin spent a year as a postdoctoral researcher at Google Research in New York where she worked with the Market Algorithm team to develop, design, and test new mechanisms and algorithms for online marketplaces.
Negin is the recipient of a 2018 Google Faculty Research Award; 2017 George B. Dantzig Dissertation Award; 2017 INFORMS Revenue Management and Pricing Section Dissertation Prize; 2018 Elwood S. Buffa Doctoral Dissertation Award from the Decision Sciences Institute; University of Southern California (USC) Ph.D. Achievement Award (2017); USC CAMS Graduate Student Prize, for excellence in research with a substantial mathematical component (2017); Honorable mention in the 2016 MSOM Student Paper Competition; and USC Provost's Ph.D. Fellowship (2011).
Negin received her BSc (2007) and MSc (2009) degrees in electrical engineering from the Sharif University of Technology, Iran, and a PhD (2017) in operations research from USC.
Beyhaghi, Hedyeh, Negin Golrezaei, Renato Paes Leme, Martin Pál, and Balasubramanian Sivan. Operations Research. Forthcoming. Online Appendix. arXiv Preprint.
Derakhshan, Mahsa, Negin Golrezaei, and Renato Paes Leme. Management Science. Forthcoming. arXiv Preprint.
Derakhshan, Mahsa, Negin Golrezaei, Vahideh Manshadi, and Vahab Mirrokni. Management Science. Forthcoming. SSRN Preprint.
Deng, Yuan, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, and Vahab Mirrokni, MIT Sloan Working Paper 6839-23. Cambridge, MA: MIT Sloan School of Management, February 2023.
Chen, Qinyi, Negin Golrezaei, and Fransisca Susan, MIT Sloan Working Paper 6840-22. Cambridge, MA: MIT Sloan School of Management, October 2022. SSRN.
Golrezaei, Negin and Evan Yao, MIT Sloan Working Paper 6838-22. Cambridge, MA: MIT Sloan School of Management, October 2022.
Online shopping is even more popular now because of the pandemic. Buyer beware: Product rankings can be based on fraudulent data like fake clicks, purchases, and reviews.
Insights on how to design methods for uncertain environments to guarantee robustness in the face of manipulation.
“...a lot of people are talking about using data to make decisions...an often- overlooked aspect of data is that it can't always be trusted."
Prof. Negin Golrezaei developed a search model that learns consumer preferences to optimize product rankings.