A Game Theoretic Analysis of the Adversarial Retrieval Setting

Author:

Ben Basat Ran,Tennenholtz Moshe,Kurland Oren

Abstract

The main goal of search engines is ad hoc retrieval: ranking documents in a corpus by their relevance to the information need expressed by a query. The Probability Ranking Principle (PRP) --- ranking the documents by their relevance probabilities --- is the theoretical foundation of most existing ad hoc document retrieval methods. A key observation that motivates our work is that the PRP does not account for potential post-ranking effects; specifically, changes to documents that result from a given ranking. Yet, in adversarial retrieval settings such as the Web, authors may consistently try to promote their documents in rankings by changing them. We prove that, indeed, the PRP can be sub-optimal in adversarial retrieval settings. We do so by presenting a novel game theoretic analysis of the adversarial setting. The analysis is performed for different types of documents (single-topic and multi-topic) and is based on different assumptions about the writing qualities of documents' authors. We show that in some cases, introducing randomization into the document ranking function yields an overall user utility that transcends that of applying the PRP.

Publisher

AI Access Foundation

Subject

Artificial Intelligence

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ranking-Incentivized Document Manipulations for Multiple Queries;Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval;2024-08-02

2. Competitive Search;Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval;2022-07-06

3. Driving the Herd;Proceedings of the 30th ACM International Conference on Information & Knowledge Management;2021-10-26

4. Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness;Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency;2021-03

5. Studying Ranking-Incentivized Web Dynamics;Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval;2020-07-25

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