Home → Magazine Archive → December 2019 (Vol. 62, No. 12) → Rethinking Search Engines and Recommendation Systems... → Abstract

Rethinking Search Engines and Recommendation Systems: a game theoretic perspective

By Moshe Tennenholtz, Oren Kurland

Communications of the ACM, Vol. 62 No. 12, Pages 66-75
10.1145/3340922

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In her popular book, Weapons of Math Destruction, data scientist Cathy O'Neil elegantly describes to the general population the danger of the data science revolution in decision making. She describes how the US News ranking of universities, which orders universities based on 15 measured properties, created new dynamics in university behavior, as they adapted to these measures, ultimately resulting in decreased social welfare. Unfortunately, the idea that data science-related algorithms, such as ranking, cause changes in behavior, and that this dynamic may lead to socially inferior outcomes, is dominant in our new online economy.

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Ranking also plays a crucial role in search engines and recommendation systems—two prominent data science applications that we focus on in this article. Search engines (for example, Google and Bing) rank Web pages, images, and other items in response to a query. Recommendation systems endorse items by ranking them using information induced from some context—for example, the Web page a user is currently browsing, a specific application the user is running on her mobile phone, or the time of day.

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