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COLLOQUIUM: Hongning Wang, "How Bad is Top-K Recommendation Under Competing Content Creators?"

Event Type
Seminar/Symposium
Sponsor
Illinois Computer Science
Location
HYBRID: 2405 Siebel Center for Computer Science or online
Virtual
wifi event
Date
Feb 5, 2024   3:30 pm  
Views
264
Originating Calendar
Computer Science Colloquium Series

Zoom: https://illinois.zoom.us/j/85772920078?pwd=OWpxWWZBcndBWnRvSm1vWldYdWRkZz09

Refreshments Provided.

Abstract: 
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' competition impacts user welfare and how the relevance-driven recommendation influences the dynamics in the long run are still largely unknown. 
 
Our recent work provides theoretical insights into these research questions. We model the creators' competition under the assumptions that: 1) the platform employs an innocuous top-K recommendation policy; 2) user decisions follow the Random Utility model; 3) content creators compete for user engagement and, without knowing their utility function in hindsight, apply arbitrary no-regret learning algorithms to update their strategies. We study the user welfare guarantee through the lens of Price of Anarchy and show that the fraction of user welfare loss due to creator competition is always upper bounded by a small constant depending on K and randomness in user decisions; we also prove the tightness of this bound. Our result discloses an intrinsic merit of the myopic approach to the recommendation, i.e., relevance-driven matching performs reasonably well in the long run, as long as users' decisions involve randomness and the platform provides reasonably many alternatives to its users.

Bio:
Dr. Hongning Wang is now an associated professor at the Department of Computer Science and Technology at Tsinghua University. Prior to that, he was the Copenhaver Associate Professor in the Department of Computer Science at the University of Virginia. He received his PhD degree in computer science at the University of Illinois at Champaign-Urbana in 2014. His research generally lies in the intersection among machine learning, data mining and information retrieval, with a special focus on sequential decision optimization and computational user modeling. His work has generated over 80 research papers in top venues in data mining and information retrieval areas. He is a recipient of 2016 National Science Foundation CAREER Award, 2020 Google Faculty Research Award, and SIGIR’2019 Best Paper Award.
 


Part of the Illinois Computer Science Speakers Series. Faculty Host: Chengxiang Zhai


Meeting ID: 857 7292 0078
Passcode: csillinois


If accommodation is required, please email <erink@illinois.edu> or <communications@cs.illinois.edu>. Someone from our staff will contact you to discuss your specific needs



 

 

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