Omer Madmon
My name is Omer, and I am a PhD candidate at the Technion - Israel Institute of Technology. My primary research field is algorithmic game theory and its applications in data science. In particular, I am interested in information design, mechanism design, and learning dynamics in the context of recommendation systems and search engines. My expertise includes mathematical economic modeling, applied ML, and integrating LLMs and GenAI into economic and strategic decision-making.
I also hold an MSc in data science and a BSc in data science and engineering, both from the Technion. During my bachelor's studies, I completed two internships at Google, working on various software engineering projects.
Session
Recommender systems play a critical role in modern digital platforms, driving personalized user experiences across e-commerce, content streaming, and social media. At the heart of these systems is the matching of users with relevant content, prompting content creators to optimize their work for visibility. This optimization leads to a competitive dynamic where creators continually adjust their content strategies until reaching a stable state, often at the expense of creative integrity. The strategic behavior of creators, in turn, influences the content available to end users, making the design of the recommendation mechanism a crucial factor in shaping user satisfaction. The platform operating the recommendation system must then balance creators' incentives, user satisfaction, and ecosystem stability to sustain long-term engagement. Understanding these interdependencies and the incentives of all participants is crucial for designing robust and sustainable recommendation systems.
In this talk, we will explore the recommendation ecosystem through the lens of game theory, addressing a fundamental question: how can we design recommendation mechanisms that guarantee the convergence of natural dynamics among strategic content creators to a stable state? We will go over basic concepts in game theory (no background needed :)) and apply them to derive a formal (yet simple) framework in which the stability of different recommendation mechanisms can be analyzed and compared. We will also present empirical evidence highlighting the trade-offs between content creators' welfare, user satisfaction, and ecosystem stability, along with practical tools for system designers to manage them effectively in alignment with business goals.
The talk is based on joint work with Idan Pipano, Itamar Reinman, and Moshe Tennenholtz:
https://arxiv.org/abs/2305.16695
https://arxiv.org/abs/2405.11517