@inproceedings{2b01477fcda747d788d04b833f4dd9da,
title = "Competitive Search",
abstract = "The Web is a canonical example of a competitive search setting that includes document authors with ranking incentives: their goal is to promote their documents in rankings induced for queries. The incentives affect some of the corpus dynamics as the authors respond to rankings by applying strategic document manipulations. This well known reality has deep consequences that go well beyond the need to fight spam. As a case in point, researchers showed using game theoretic analysis that the probability ranking principle is not optimal in competitive retrieval settings; specifically, it leads to reduced topical diversity in the corpus. We provide a broad perspective on recent work on competitive retrieval settings, argue that this work is the tip of the iceberg, and pose a suite of novel research directions; for example, a general game theoretic framework for competitive search, methods of learning-to-rank that account for post-ranking effects, approaches to automatic document manipulation, addressing societal aspects and evaluation.",
keywords = "competitive search, game theory, search engine optimization",
author = "Oren Kurland and Moshe Tennenholtz",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 ; Conference date: 11-07-2022 Through 15-07-2022",
year = "2022",
month = jul,
day = "6",
doi = "https://doi.org/10.1145/3477495.3532771",
language = "الإنجليزيّة",
series = "SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval",
pages = "2838--2849",
booktitle = "SIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval",
}