Abstract
Content recommendations on big internet platforms such as YouTube are supported by crowd-based recommender systems. Users are presented with what other users in comparable situations spent time on. There are several examples, however, where sequences of suggested content quickly degenerated towards only slightly related, sometimes problematic content that we define as contextually inappropriate. We try to identify the basis of this effect from both the user and the technical side, and set up a simple simulation system in order to better understand the interactions. Simulation results provide evidence that autoplay is an especially problematic feature, but that completely preventing inappropriate suggestions is technically very hard if not infeasible because it is the nature of a recommendation system to take into account feedback from the user and adapt to it. We also propose some possible measures to mitigate the problem.
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Notes
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For a general idea on how embeddings work see: https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526.
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It may be difficult to imagine such a huge space since we experience the world around us as only three dimensional. In principle we could also break this high dimensional space into a large number of 3D spaces, in our case around 85 3D cubes would be necessary. A point (video in our case) would then have a 3D position in each of these cubes and all cubes together provide its position in 255 dimensions.
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Stöcker, C., Preuss, M. (2020). Riding the Wave of Misclassification: How We End up with Extreme YouTube Content. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_25
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