review articles

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.

In the industrial search and information retrieval community, the practice of adapting content to satisfy a search engine’s ranking function or that of a recommendation engine has been typically referred to as search engine optimization (SEO), which has become one of the most profitable professions in the data science era. Accordingly, there has been much work on devising algorithms that block spammers and guarantee that content presented to users is of high quality. Yet, virtually all retrieval and recommendation algorithms ignore post-ranking/recom-mendation effects (that is, manipulations applied to items) on the corpus. As it turns out, this reality results in underoptimized fundamental paradigms for devising search and recommendation algorithms as we discuss here. For example, a publisher of a Web page can try and mimic other Web pages to promote her page in rankings, thereby potentially causing content

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.

Rethinking Search
Engines and
Recommendation
Systems:
A Game Theoretic
Perspective
DOI: 10.1145/3340922

Novel approaches draw on the strength of game theoretic mechanism design.

BY MOSHE TENNENHOLTZ AND OREN KURLAND

key insights

˽ An overall modeling and analysis of the search and recommendation systems ecosystems (publishers, users, mediators, and competitors) is essential for their efficient design.

˽ Strategic content dynamics may lead to failure of classical principles of search and recommendation systems in maximizing social welfare; these principles can/should be revisited.

˽ Game-theoretic mechanism design can be adapted to the design of economically efficient search and recommendation systems.