Abstract
Recommender Systems (RSs) research has become interested in assessing the system effect on the actual choices of their users: the distribution and the quality of the choices. By simulating users’ choices, influenced by RSs, it was shown that algorithmic biases, such as the tendency to recommend popular items, are transferred to the users’ choices.
In this paper we conjecture that the effect of an RS on the quality and distribution of the users’ choices can also be influenced by the users’ tendency to prefer certain types of items, i.e., popular, recent, or highly rated items. To quantify this impact, we define alternative Choice Models (CMs) and we simulate their effect when users are exposed to recommendations. We find that RS biases can also be enforced by the CM, e.g., the tendency to concentrate the choices on a restricted number of items. Moreover, we discover that the quality of the choices can be jeopardised by a CM. We also find that for some RSs the impact of the CM is less prominent and their biases are not modified by the CM. This study show the importance of assessing algorithmic biases in conjunction with a proper model of users’ behaviour.
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.: Beyond personalization: Research directions in multistakeholder recommendation. arXiv preprint arXiv:1905.01986 (2019)
Bartels, J., Reinders, M.J.: Consumer innovativeness and its correlates: a propositional inventory for future research. J. Bus. Res. 64(6), 601–609 (2011)
Bountouridis, D., Harambam, J., Makhortykh, M., Marrero, M., Tintarev, N., Hauff, C.: Siren: a simulation framework for understanding the effects of recommender systems in online news environments. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 150–159. ACM (2019)
Chaney, A.J., Stewart, B.M., Engelhardt, B.E.: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 224–232 (2018)
Ciampaglia, G.L., Nematzadeh, A., Menczer, F., Flammini, A.: How algorithmic popularity bias hinders or promotes quality. Sci. Rep. 8(1), 1–7 (2018)
Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manage. Sci. 55(5), 697–712 (2009)
Funk, S.: Netflix update: try this at home (2006)
Gravino, P., Monechi, B., Loreto, V.: Towards novelty-driven recommender systems. Comptes Rendus Physique 20(4), 371–379 (2019)
Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10(Dec), 2935–2962 (2009)
Hazrati, N., Ricci, F.: Recommender systems effect on the evolution of users’ choices distribution. Inf. Process. Manage. 59(1), 102766 (2022)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference On World Wide Web, pp. 507–517 (2016)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272. IEEE (2008)
Kowald, D., Schedl, M., Lex, E.: The unfairness of popularity bias in music recommendation: a reproducibility study. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 35–42. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_5
Levin, J., Milgrom, P.: Introduction to choice theory (2004). http://web.stanford.edu/$~$jdlevin/Econd/20202
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., Joachims, T.: Recommendations as treatments: debiasing learning and evaluation. arXiv preprint arXiv:1602.05352 (2016)
Szlávik, Z., Kowalczyk, W., Schut, M.: Diversity measurement of recommender systems under different user choice models. In: Fifth International AAAI Conference on Weblogs and Social Media (2011)
Vargas, S.: Novelty and diversity evaluation and enhancement in recommender systems. Ph.D. thesis, Ph.D. Dissertation. Universidad Autónoma de Madrid (2015)
Zhang, J., Adomavicius, G., Gupta, A., Ketter, W.: Consumption and performance: understanding longitudinal dynamics of recommender systems via an agent-based simulation framework. Inf. Syst. Res. 31(1), 76–101 (2020)
Zhu, Z., Wang, J., Caverlee, J.: Measuring and mitigating item under-recommendation bias in personalized ranking systems. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–458 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hazrati, N., Ricci, F. (2022). The Impact of Recommender System and Users’ Behaviour on Choices’ Distribution and Quality. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham. https://doi.org/10.1007/978-3-031-09316-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-09316-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09315-9
Online ISBN: 978-3-031-09316-6
eBook Packages: Computer ScienceComputer Science (R0)