Abstract
The success of Google’s PageRank algorithm popularized graphs as a tool to model the web’s navigability. At that time, the web topology was resulting from human edition of hyper-links. Nowadays, that topology is mostly resulting from algorithms. In this paper, we propose to study the topology realized by a class of such algorithms: recommenders. By modeling the output of recommenders as graphs, we show that a vast array of topological observations become easily accessible, using a simple web-crawler. We give models and illustrations for those graph representations. We then propose a graph-based methodology for addressing an algorithmic transparency problem: recommendation bias detection. We illustrate this approach on YouTube crawls, targeting the prediction of “Recommended for you” links.
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Notes
- 1.
We note that these graph structures and their dynamics relate them to time-varying networks and time-varying graphs [3].
- 2.
“Lady Gaga’s FULL Pepsi Zero Sugar Super Bowl LI Halftime Show | NFL”, https://www.youtube.com/watch?v=txXwg712zw4.
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Le Merrer, E., Trédan, G. (2018). The Topological Face of Recommendation. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_72
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DOI: https://doi.org/10.1007/978-3-319-72150-7_72
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