Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents (2305.16695v4)

Published 26 May 2023 in cs.GT and cs.IR

Abstract: We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents. We show that the commonly used Probability Ranking Principle (PRP) ranking scheme results in an unstable environment where games often fail to reach pure Nash equilibrium. We propose two families of ranking functions that do not adhere to the PRP principle. We provide both theoretical and empirical evidence that these methods lead to a stable search ecosystem, by providing positive results on the learning dynamics convergence. We also define the publishers' and users' welfare, demonstrate a possible publisher-user trade-off, and provide means for a search system designer to control it. Finally, we show how instability harms long-term users' welfare.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Fair clustering via equitable group representations. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 504–514, 2021.
  2. Fiduciary bandits. In International Conference on Machine Learning, pages 518–527. PMLR, 2020.
  3. The probability ranking principle is not optimal in adversarial retrieval settings. In Proceedings of the 2015 International Conference on The Theory of Information Retrieval, pages 51–60, 2015.
  4. Nonlinear programming. athena scientific belmont. Massachusets, USA, 1999.
  5. From recommendation systems to facility location games. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 1772–1779, 2019.
  6. Convergence of learning dynamics in information retrieval games. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 1780–1787, 2019.
  7. Protecting the protected group: Circumventing harmful fairness. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 5176–5184, 2021.
  8. A game-theoretic approach to recommendation systems with strategic content providers. Advances in Neural Information Processing Systems, 31, 2018.
  9. Query-performance prediction using minimal relevance feedback. In Proceedings of the 2013 Conference on the Theory of Information Retrieval, pages 14–21, 2013.
  10. A game theoretic analysis of the adversarial retrieval setting. Journal of Artificial Intelligence Research, 60:1127–1164, 2017.
  11. The dynamics of reinforcement learning in cooperative multiagent systems. AAAI/IAAI, 1998(746-752):2, 1998.
  12. Convergence of position auctions under myopic best-response dynamics. ACM Transactions on Economics and Computation (TEAC), 2(3):1–20, 2014.
  13. Fair clustering through fairlets. Advances in neural information processing systems, 30, 2017.
  14. Adaptive game playing using multiplicative weights. Games and Economic Behavior, 29(1-2):79–103, 1999.
  15. Modeling content creator incentives on algorithm-curated platforms. arXiv preprint arXiv:2206.13102, 2022.
  16. Strategic classification. In Proceedings of the 2016 ACM conference on innovations in theoretical computer science, pages 111–122, 2016.
  17. Accurately interpreting clickthrough data as implicit feedback. In Acm Sigir Forum, volume 51, pages 4–11. Acm New York, NY, USA, 2017.
  18. Supply-side equilibria in recommender systems. arXiv preprint arXiv.2206.13489, 2023.
  19. Competitive search. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2838–2849, 2022.
  20. Generalized strategic classification and the case of aligned incentives. In International Conference on Machine Learning, pages 12593–12618. PMLR, 2022.
  21. The impacts of time constraint on users’ search strategy during search process. Proceedings of the Association for Information Science and Technology, 53(1):1–9, 2016.
  22. Optimizing long-term social welfare in recommender systems: A constrained matching approach. In International Conference on Machine Learning, pages 6987–6998. PMLR, 2020.
  23. Convergence to equilibria in plurality voting. In Proceedings of the AAAI conference on artificial intelligence, volume 24, pages 823–828, 2010.
  24. Potential games. Games and economic behavior, 14(1):124–143, 1996.
  25. Abraham Neyman. Correlated equilibrium and potential games. International Journal of Game Theory, 26:223–227, 1997.
  26. Strategic representation. In International Conference on Machine Learning, pages 16331–16352. PMLR, 2022.
  27. Segmentation, incentives, and privacy. Mathematics of Operations Research, 43(4):1252–1268, 2018.
  28. Understanding the behaviors of bert in ranking. arXiv preprint arXiv:1904.07531, 2019.
  29. Stephen E Robertson. The probability ranking principle in ir. Journal of documentation, 1977.
  30. Information retrieval meets game theory: The ranking competition between documents’ authors. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 465–474, 2017.
  31. Rethinking incentives in recommender systems: Are monotone rewards always beneficial? Advances in Neural Information Processing Systems, 36, 2024.
  32. Dense text retrieval based on pretrained language models: A survey. arXiv preprint arXiv:2211.14876, 2022.
  33. Learning to retrieve: How to train a dense retrieval model effectively and efficiently. arXiv preprint arXiv:2010.10469, 2020.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets