Abstract
Currently, the research on the fairness of recommender systems has expanded beyond considering only the interests of users and product providers. However, the stakeholders of recommender systems go beyond just users and product providers; the platform that provides the recommendation service is also an important player whose interests are currently overlooked in the recommendation algorithm. In this study, we address this gap by considering the potential gains of the platform in addition to recommendation quality and fairness among providers. We analyze the theoretical relationships between recommendation quality, fairness among providers, and the platform’s potential gains. Subsequently, we propose a fair recommendation strategy that takes into account the interests of all three parties. Through experiments conducted on a real-world dataset, we demonstrate that our models successfully achieve the desired design goals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdollahpouri, H., et al.: Multistakeholder recommendation: survey and research directions. User Model. User-Adap. Inter. 30(1), 127–158 (2020)
Abdollahpouri, H., Burke, R.: Multi-stakeholder recommendation and its connection to multi-sided fairness (2019)
Abdollahpouri, H., Burke, R., Mobasher, B.: Recommender systems as multistakeholder environments. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 347–348. ACM, New York (2017)
Bose, A.J., Hamilton, W.L.: Compositional fairness constraints for graph embeddings (2019)
Budish, E.: The combinatorial assignment problem: approximate competitive equilibrium from equal incomes. J. Polit. Econ. 119(6), 1061–1061 (2010)
Burke, R.: Multisided fairness for recommendation (2017)
Calmon, F.P., Wei, D., Vinzamuri, B., Ramamurthy, K.N., Varshney, K.R.: Optimized pre-processing for discrimination prevention. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3995–4004. Curran Associates Inc., Red Hook (2017)
Do, V., Corbett-Davies, S., Atif, J., Usunier, N.: Online certification of preference-based fairness for personalized recommender systems. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 6, pp. 6532–6540 (2022)
Fu, Z., et al.: Fairness-aware explainable recommendation over knowledge graphs. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 69–78. Association for Computing Machinery, New York (2020)
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, pp. 348–358. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3351095.3372848
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Karako, C., Manggala, P.: Using image fairness representations in diversity-based re-ranking for recommendations. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 23–28. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3213586.3226206
Liu, W., Burke, R.: Personalizing fairness-aware re-ranking (2018)
Liu Peng, W.C.: Advertising Computing: Market and Technology of Realizing Internet Business. Posts & Telecom, Bei**g (2019)
Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: FairRec: two-sided fairness for personalized recommendations in two-sided platforms. In: Proceedings of The Web Conference 2020, pp. 1194–1204. ACM, New York (2020). https://doi.org/10.1145/3366423.3380196
Patro, G.K., Chakraborty, A., Ganguly, N., Gummadi, K.: Incremental fairness in two-sided market platforms: on smoothly updating recommendations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 181–188 (2020)
Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2219–228. ACM, New York (2018). https://doi.org/10.1145/3219819.3220088
Sühr, T., Biega, A.J., Zehlike, M., Gummadi, K.P., Chakraborty, A.: Two-sided fairness for repeated matchings in two-sided markets: a case study of a ride-hailing platform. In: the 25th ACM SIGKDD International Conference, pp. 3082–3092 (2019)
Wu, C., Wu, F., Wang, X., Huang, Y., **e, X.: FairRec: fairness-aware news recommendation with decomposed adversarial learning (2020)
Wu, Y., Cao, J., Xu, G., Tan, Y.: 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, pp. 1013–1022. Association for Computing Machinery, New York (2021)
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 and Data Mining, pp. 2536–2544. ACM, 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 (2017). https://doi.org/10.1145/3085504.3085526
Zhao, H.Y., Zhou, P., Chen, Q.K., Cao, J.: Fairness evaluation methods in recommender system: current research and prospects. J. Chin. Comput. Syst. 43(3), 456–465 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, H., Zhou, P., Cao, J., Zhu, N. (2024). FRS4CPP: A Fair Recommendation Strategy Considering Interests of Users, Providers and Platform. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_27
Download citation
DOI: https://doi.org/10.1007/978-981-99-9637-7_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9636-0
Online ISBN: 978-981-99-9637-7
eBook Packages: Computer ScienceComputer Science (R0)
