Recommender systems are a predominant feature of online platforms and one of the most widespread applications of artificial intelligence. A new model captures information dynamics driven by algorithmic recommendations and offers ways to ensure that users are exposed to diverse content and information.
References
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B. & Burke, R. In Proc. 29th ACM International Conference on Information & Knowledge Management 2145–2148 (ACM, 2020).
Smith, J. J., Beattie, L. & Cramer, H. In Proc. ACM Web Conference 2023 3648–3659 (ACM, 2023).
Espín-Noboa, L., Wagner, C., Strohmaier, M. & Karimi, F. Sci. Rep. 12, 2012 (2022).
Chaney, A. J., Stewart, B. M. & Engelhardt, B. E. In Proc. 12th ACM Conference on Recommender Systems 224–232 (ACM, 2018).
Sunstein, C. R. Infotopia: How Many Minds Produce Knowledge (Oxford Univ. Press, 2006).
Piao, J., Liu, J., Zhang, F., Su, J. & Li, Y. Nat. Mach. Intell. https://doi.org/10.1038/s42256-023-00731-4 (2023).
Castells, P., Hurley, N. & Vargas, S. In Recommender Systems Handbook (eds. Ricci, F. et al.) 603–646 (Springer, 2021).
Möller, J., Trilling, D., Helberger, N. & van Es, B. Info. Commun. Soc. 21, 959–977 (2018).
Huang, J., Oosterhuis, H., De Rijke, M. & Van Hoof, H. In Proc. 14th ACM Conference on Recommender Systems 190–199 (ACM, 2020).
Santos, F. P., Lelkes, Y. & Levin, S. A. Proc. Natl Acad. Sci. USA 118, e2102141118 (2021).
Bountouridis, D. et al. In Proc. 2019 Conference on Fairness, Accountability, and Transparency 150–159 (ACM, 2019).
Guess, A. M. et al. Science 381, 398–404 (2023).
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Santos, F.P. How to break information cocoons. Nat Mach Intell 5, 1338–1339 (2023). https://doi.org/10.1038/s42256-023-00758-7
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DOI: https://doi.org/10.1038/s42256-023-00758-7
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