论文标题
基于协作过滤的推荐系统中的回声室
Echo Chambers in Collaborative Filtering Based Recommendation Systems
论文作者
论文摘要
推荐系统基于现代几乎所有在线内容的服务。从YouTube和Netflix的建议到Facebook Feed和Google搜索，这些系统旨在将内容过滤到用户的预测偏好。最近，这些系统对它们对内容多样性，社会两极分化和公共话语健康的影响面临越来越多的批评。在这项工作中，我们模拟了Movielens数据集中用户的协作过滤算法给出的建议。我们发现，长时间接触系统生成的建议大大降低了内容多样性，将单个用户转移到以狭窄范围内容为特征的“回声室”。此外，我们的工作表明，一旦建立了这些回声室，个人用户就很难仅通过操纵自己的评级向量来爆发。
Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.
