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Exposure bias is a well-known issue in recommender systems where the exposureis not fairly distributed among items in the recommendation results. This isespecially problematic when bias is amplified over time as a few items (e.g.,popular ones) are repeatedly over-represented in recommendation lists andusers' interactions with those items will amplify bias towards those items overtime resulting in a feedback loop. This issue has been extensively studied inthe literature in static recommendation environment where a single round ofrecommendation result is processed to improve the exposure fairness. However,less work has been done on addressing exposure bias in a dynamic recommendationsetting where the system is operating over time, the recommendation model andthe input data are dynamically updated with ongoing user feedback onrecommended items at each round. In this paper, we study exposure bias in adynamic recommendation setting. Our goal is to show that existing biasmitigation methods that are designed to operate in a static recommendationsetting are unable to satisfy fairness of exposure for items in long run. Inparticular, we empirically study one of these methods and show that repeatedlyapplying this method fails to fairly distribute exposure among items in longrun. To address this limitation, we show how this method can be adapted toeffectively operate in a dynamic recommendation setting and achieve exposurefairness for items in long run. Experiments on a real-world dataset confirmthat our solution is superior in achieving long-term exposure fairness for theitems while maintaining the recommendation accuracy.

Fairness of Exposure in Dynamic Recommendation