Recommender systems, which offer personalized suggestions to users, power many of today's social media, e-commerce and entertainment. However, these systems have been known to intellectually isolate users from a variety of perspectives, or cause filter bubbles. In our work, we characterize and mitigate this filter bubble effect. We do so by classifying various datapoints based on their user-item interaction history and calculating the influences of the classified categories on each other using the well known TracIn method. Finally, we mitigate this filter bubble effect without compromising accuracy by carefully retraining our recommender system.
翻译:向用户提供个性化建议的建议系统,赋予当今许多社交媒体、电子商业和娱乐活动的权力。然而,这些系统众所周知,在知识上从各种角度将用户隔离开来,或造成过滤泡沫。在我们的工作中,我们根据用户-项目互动历史对各种数据点进行分类,并使用众所周知的TracIn方法计算分类类别对彼此的影响。最后,我们通过仔细再培训我们的推荐人系统,在不损害准确性的情况下减轻这种过滤泡沫效应。