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Measuring User Recourse in a Dynamic Recommender System
Dilys Dickson · Elliot Creager

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in ICML Workshop on Algorithmic Recourse »

From online searches to suggested videos in social media, recommendation systems are heavily relied upon to mediate access to digital information. Concerns have been raised about these systems over the potential for feedback loops that can create unintended consequences such as echo-chambers, filter bubbles and polarization in the digital space. In this paper, we measure the effect of prolonged exposure to recommendation on availability of diverse suggested content to the user. We use the definition of reachability (or user recourse) of Dean et al 2020, as the proportion of unseen items that could be recommended to the user in the future, which can be approximated using knowledge of the embedding space geometry for linear recommenders. Whereas previous work assumed a static recommender, we study the case where the recommender can change over time, either by training for longer given a fixed dataset, or dynamically updating its training online through interactions with users. We find that dynamic changes to the recommender system do indeed affect the recourse available to users.

Author Information

Dilys Dickson (African Institute of Mathematical Sciences)
Elliot Creager (University of Toronto)

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