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Algorithmic recommendations

How to break information cocoons

Nature Machine Intelligence volume 5, pages 1338–1339 (2023)Cite this article

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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.

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Fig. 1: Emerging information cocoons and ways to break them.

References

  1. Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B. & Burke, R. In Proc. 29th ACM International Conference on Information & Knowledge Management 2145–2148 (ACM, 2020).

  2. Smith, J. J., Beattie, L. & Cramer, H. In Proc. ACM Web Conference 2023 3648–3659 (ACM, 2023).

  3. Espín-Noboa, L., Wagner, C., Strohmaier, M. & Karimi, F. Sci. Rep. 12, 2012 (2022).

    Article  Google Scholar 

  4. Chaney, A. J., Stewart, B. M. & Engelhardt, B. E. In Proc. 12th ACM Conference on Recommender Systems 224–232 (ACM, 2018).

  5. Sunstein, C. R. Infotopia: How Many Minds Produce Knowledge (Oxford Univ. Press, 2006).

  6. Piao, J., Liu, J., Zhang, F., Su, J. & Li, Y. Nat. Mach. Intell. https://doi.org/10.1038/s42256-023-00731-4 (2023).

    Article  Google Scholar 

  7. Castells, P., Hurley, N. & Vargas, S. In Recommender Systems Handbook (eds. Ricci, F. et al.) 603–646 (Springer, 2021).

  8. Möller, J., Trilling, D., Helberger, N. & van Es, B. Info. Commun. Soc. 21, 959–977 (2018).

    Article  Google Scholar 

  9. Huang, J., Oosterhuis, H., De Rijke, M. & Van Hoof, H. In Proc. 14th ACM Conference on Recommender Systems 190–199 (ACM, 2020).

  10. Santos, F. P., Lelkes, Y. & Levin, S. A. Proc. Natl Acad. Sci. USA 118, e2102141118 (2021).

    Article  Google Scholar 

  11. Bountouridis, D. et al. In Proc. 2019 Conference on Fairness, Accountability, and Transparency 150–159 (ACM, 2019).

  12. Guess, A. M. et al. Science 381, 398–404 (2023).

    Article  Google Scholar 

Download references

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  1. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands

    Fernando P. Santos

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  1. Fernando P. Santos
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Correspondence to Fernando P. Santos.

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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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