Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 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

Vertical Allocation-based Fair Exposure Amortizing in Ranking (2204.03046v2)

Published 6 Apr 2022 in cs.IR

Abstract: Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by ranking items only according to relevance will lead to unfair distribution of exposure for items, followed by unfair opportunities and economic gains for item producers/providers. Such unfairness will force providers to leave the system and discourage new providers from coming in. Eventually, fewer purchase options would be left for consumers, and the utilities of both consumers and providers would be harmed. Thus, to maintain a balance between ranking relevance and fairness is crucial for both parties. In this paper, we focus on the exposure fairness in ranking services. We demonstrate that existing methods for amortized fairness optimization could be suboptimal in terms of fairness-relevance tradeoff because they fail to utilize the prior knowledge of consumers. We further propose a novel algorithm named Vertical Allocation-based Fair Exposure Amortizing in Ranking, or VerFair, to reach a better balance between exposure fairness and ranking performance. Extensive experiments on three real-world datasets show that VerFair significantly outperforms state-of-the-art fair ranking algorithms in fairness-performance trade-offs from both the individual level and the group level.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (66)
  1. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction 30, 1 (2020), 127–158.
  2. Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems. 42–46.
  3. Recommender systems as multistakeholder environments. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. 347–348.
  4. Informed recommender: Basing recommendations on consumer product reviews. IEEE Intelligent systems 22, 3 (2007), 39–47.
  5. Estimating position bias without intrusive interventions. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 474–482.
  6. Unbiased learning to rank with unbiased propensity estimation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 385–394.
  7. Unbiased learning to rank: online or offline? ACM Transactions on Information Systems (TOIS) 39, 2 (2021), 1–29.
  8. Topicmf: simultaneously exploiting ratings and reviews for recommendation.. In Aaai, Vol. 14. 2–8.
  9. Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2212–2220.
  10. Equity of attention: Amortizing individual fairness in rankings. In The 41st international acm sigir conference on research & development in information retrieval. 405–414.
  11. Gender Fairness in Information Retrieval Systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3436–3439.
  12. Toward fair recommendation in two-sided platforms. ACM Transactions on the Web (TWEB) 16, 2 (2021), 1–34.
  13. Ranking with fairness constraints. arXiv preprint arXiv:1704.06840 (2017).
  14. Click models for web search. Synthesis lectures on information concepts, retrieval, and services 7, 3 (2015), 1–115.
  15. An experimental comparison of click position-bias models. In Proceedings of the 2008 international conference on web search and data mining. 87–94.
  16. Two-sided fairness in rankings via Lorenz dominance. Advances in Neural Information Processing Systems 34 (2021), 8596–8608.
  17. Georges E Dupret and Benjamin Piwowarski. 2008. A user browsing model to predict search engine click data from past observations.. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 331–338.
  18. Racial discrimination in the sharing economy: Evidence from a field experiment. American Economic Journal: Applied Economics 9, 2 (2017), 1–22.
  19. Overview of the TREC 2022 Fair Ranking Track. arXiv preprint arXiv:2302.05558 (2023).
  20. Certifying and removing disparate impact. In proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 259–268.
  21. FAIR: Fairness-aware information retrieval evaluation. Journal of the Association for Information Science and Technology 73, 10 (2022), 1461–1473.
  22. Explainable Fairness in Recommendation. arXiv preprint arXiv:2204.11159 (2022).
  23. The Winner Takes it All: Geographic Imbalance and Provider (Un) fairness in Educational Recommender Systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1808–1812.
  24. Efficient multiple-click models in web search. In Proceedings of the second acm international conference on web search and data mining. 124–131.
  25. Equality of opportunity in supervised learning. In Advances in neural information processing systems. 3315–3323.
  26. Translation-based recommendation. In Proceedings of the eleventh ACM conference on recommender systems. 161–169.
  27. Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (2002), 422–446.
  28. Yiling Jia and Hongning Wang. 2021. Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank. arXiv preprint arXiv:2111.00735 (2021).
  29. Accurately interpreting clickthrough data as implicit feedback. In ACM SIGIR Forum, Vol. 51. Acm New York, NY, USA, 4–11.
  30. Fairness-aware classifier with prejudice remover regularizer. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 35–50.
  31. Chen Karako and Putra Manggala. 2018. Using image fairness representations in diversity-based re-ranking for recommendations. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. 23–28.
  32. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning. 2564–2572.
  33. Anja Lambrecht and Catherine Tucker. 2019. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science 65, 7 (2019), 2966–2981.
  34. Ratings meet reviews, a combined approach to recommend. In Proceedings of the 8th ACM Conference on Recommender systems. 105–112.
  35. Weiwen Liu and Robin Burke. 2018. Personalizing fairness-aware re-ranking. arXiv preprint arXiv:1809.02921 (2018).
  36. Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems. 165–172.
  37. Controlling Fairness and Bias in Dynamic Learning-to-Rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20). Association for Computing Machinery, New York, NY, USA, 429–438. https://doi.org/10.1145/3397271.3401100
  38. Cpfair: Personalized consumer and producer fairness re-ranking for recommender systems. arXiv preprint arXiv:2204.08085 (2022).
  39. Harrie Oosterhuis. 2021. Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1023–1032.
  40. FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In Proceedings of The Web Conference 2020. 1194–1204.
  41. Fair ranking: a critical review, challenges, and future directions. arXiv preprint arXiv:2201.12662 (2022).
  42. Fairness in Rankings and Recommendations: An Overview. arXiv preprint arXiv:2104.05994 (2021).
  43. Filip Radlinski and Thorsten Joachims. 2006. Minimally invasive randomization for collecting unbiased preferences from clickthrough logs. In Proceedings of the national conference on artificial intelligence, Vol. 21. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 1406.
  44. Amifa Raj and Michael D Ekstrand. 2022. Measuring Fairness in Ranked Results: An Analytical and Empirical Comparison. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 726–736.
  45. Yuta Saito and Thorsten Joachims. 2022. Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1514–1524.
  46. Fairness in package-to-group recommendations. In Proceedings of the 26th International Conference on World Wide Web. 371–379.
  47. Ashudeep Singh and Thorsten Joachims. 2018. Fairness of exposure in rankings. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2219–2228.
  48. Ashudeep Singh and Thorsten Joachims. 2019. Policy learning for fairness in ranking. In Advances in Neural Information Processing Systems. 5426–5436.
  49. Fairness in ranking under uncertainty. Advances in Neural Information Processing Systems 34 (2021).
  50. Rating-boosted latent topics: Understanding users and items with ratings and reviews.. In IJCAI, Vol. 16. 2640–2646.
  51. Automated directed fairness testing. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. 98–108.
  52. Fast online ranking with fairness of exposure. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 2157–2167.
  53. Position bias estimation for unbiased learning to rank in personal search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 610–618.
  54. Joint multisided exposure fairness for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 703–714.
  55. TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers. https://doi.org/10.48550/ARXIV.2104.09024
  56. Tfrom: A two-sided fairness-aware recommendation model for both customers and providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1013–1022.
  57. P-MMF: Provider Max-min Fairness Re-ranking in Recommender System. In Proceedings of the ACM Web Conference 2023. 3701–3711.
  58. Reinforcement Learning to Rank with Coarse-grained Labels. arXiv preprint arXiv:2208.07563 (2022).
  59. Tao Yang and Qingyao Ai. 2021. Maximizing Marginal Fairness for Dynamic Learning to Rank. In Proceedings of the Web Conference 2021. 137–145.
  60. Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach. arXiv preprint arXiv:2305.16606 (2023).
  61. Can clicks be both labels and features? Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 6–17.
  62. FARA: Future-aware Ranking Algorithm for Fairness Optimization. arXiv preprint arXiv:2305.16637 (2023).
  63. Marginal-Certainty-aware Fair Ranking Algorithm. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 24–32.
  64. Fa* ir: A fair top-k ranking algorithm. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1569–1578.
  65. Wenbin Zhang and Eirini Ntoutsi. 2019. Faht: an adaptive fairness-aware decision tree classifier. arXiv preprint arXiv:1907.07237 (2019).
  66. Fairness-aware tensor-based recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 1153–1162.
Citations (5)
View on Semantic Scholar

Summary

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

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