Abstract
Several recent works have highlighted how search and recommender systems exhibit bias along different dimensions. Counteracting this bias and bringing a certain amount of fairness in search is crucial to not only creating a more balanced environment that considers relevance and diversity but also providing a more sustainable way forward for both content consumers and content producers. This short paper examines some of the recent works to define relevance, diversity, and related concepts. Then, it focuses on explaining the emerging concept of fairness in various recommendation settings. In doing so, this paper presents comparisons and highlights contracts among various measures, and gaps in our conceptual and evaluative frameworks.
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References
Protected Group. https://en.wikipedia.org/wiki/Protected_group. Accessed 20 Jan 2020
What is Search Neutrality? https://hackernoon.com/what-is-search-neutrality-d05cc30c6b3e. Accessed 20 Jan 2020
Women less likely to be shown ads for high-paid jobs on Google, study shows. https://www.theguardian.com/technology/2015/jul/08/women-less-likely-ads-high-paid-jobs-google-study. Accessed 20 Jan 2020
Abdollahpouri, H., Burke, R., Mobasher, B.: Recommender systems as multistakeholder environments. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, UMAP 2017. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3079628.3079657
Abdollahpouri, H., Burke, R.D.: Multi-stakeholder recommendation and its connection to multi-sided fairness. ArXiv abs/1907.13158 (2019)
Amigó, E., Spina, D., Carrillo-de Albornoz, J.: An axiomatic analysis of diversity evaluation metrics: Introducing the rank-biased utility metric. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3209978.3210024
Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3292500.3330745
Burke, R.: Multisided Fairness for Recommendation. arXiv:1707.00093 [cs], July 2017
Chakraborty, A., Patro, G.K., Ganguly, N., Gummadi, K.P., Loiseau, P.: Equality of voice: towards fair representation in crowdsourced top-k recommendations. In: Proceedings of the Conference on Fairness, Accountability, and Transparency. FAT* 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3287560.3287570
Datta, A., Tschantz, M.C., Datta, A.: Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. ArXiv abs/1408.6491 (2014)
Ferraro, A., Bogdanov, D., Serra, X., Yoon, J.J.: Artist and style exposure bias in collaborative filtering based music recommendations. ArXiv abs/1911.04827 (2019)
Gao, R., Shah, C.: Toward creating a fairer ranking in search engine results. Inf. Process. Manag. 57, (2020). https://doi.org/10.1016/j.ipm.2019.102138
Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3292500.3330691
Grimmelmann, J.: Some skepticism about search neutrality. Essays on the Future of the Internet, The Next Digital Decade (2011)
Heidari, H., Krause, A.: Preventing disparate treatment in sequential decision making. In: IJCAI (2018)
Ilvento, C., Jagadeesan, M., Chawla, S.: Multi-category fairness in sponsored search auctions. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3351095.3372848
Kuhlman, C., VanValkenburg, M., Rundensteiner, E.: Fare: diagnostics for fair ranking using pairwise error metrics. In: The World Wide Web Conference, WWW 2019. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3308558.3313443
Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F.: Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3269206.3272027
Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: Estimating the click-through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1242572.1242643
Sakai, T., Craswell, N., Song, R., Robertson, S.E., Dou, Z., Lin, C.Y.: Simple evaluation metrics for diversified search results. In: EVIA@NTCIR (2010)
Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Fairness in package-to-group recommendations. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052612
Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3219819.3220088
Verma, S., Rubin, J.: Fairness definitions explained. In: Proceedings of the International Workshop on Software Fairness, FairWare 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3194770.3194776
Wan, M., Ni, J., Misra, R., McAuley, J.: Addressing marketing bias in product recommendations. In: Proceedings of the 13th International Conference on Web Search and Data Mining, WSDM 2020. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3336191.3371855
Wu, Y., Zhang, L., Wu, X.: On discrimination discovery and removal in ranked data using causal graph. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3219819.3220087
Yang, K., Stoyanovich, J.: Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, SSDBM 2017. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3085504.3085526
Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fa*ir: a fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3132847.3132938
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Verma, S., Gao, R., Shah, C. (2020). Facets of Fairness in Search and Recommendation. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Bias and Social Aspects in Search and Recommendation. BIAS 2020. Communications in Computer and Information Science, vol 1245. Springer, Cham. https://doi.org/10.1007/978-3-030-52485-2_1
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DOI: https://doi.org/10.1007/978-3-030-52485-2_1
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