Research:Prioritization of Wikipedia Articles/Importance/SuggestBot
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The goal of this experiment is to explore editors' willingness to balance personalization with content equity in edit recommender systems. Our overarching research question is something like "can we use recommender systems to increase fairness in Wikipedia's content without sacrificing user engagement?" Our area of study is SuggestBot, which is an open-source recommender system that helps match Wikipedia editors with tasks they might be interested in completing. Prior work provides various methods for making recommendations more fair at a global level (e.g., recommending biographical articles about women and men at more similar rates) with minimal loss to how relevant the recommendations are to those receiving them. We would like to test the effectiveness of these methods in the Wikipedia task-routing context by modifying SuggestBot’s algorithm to incorporate them, deploying the modified algorithm to a subset of SuggestBot’s recommendations, and comparing the uptake of these new recommendations with those that are generated using SuggestBot’s standard algorithm.
Our experiment ran from September 7th through December 31st, 2022. On average, about half of recommendations provided to the user were modified in some way. Our final dataset includes 39,990 recommendations (1,333 sets of 30) across 281 unique users.