论文标题
量化推荐系统的影响
Quantifying the Effects of Recommendation Systems
论文作者
论文摘要
今天的推荐系统对消费者行为和对世界的个人看法产生了强烈的影响。通过使用协作过滤（CF）方法来创建建议，它生成了一个连续的反馈回路，在该循环中，用户行为在算法系统中被放大。流行物品更频繁地推荐，从而产生影响和改变用户喜好的偏见。为了可视化和比较不同的偏见，我们将分析推荐系统的效果，并量化它们引起的不平等现象。
Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedback loop in which user behavior becomes magnified in the algorithmic system. Popular items get recommended more frequently, creating the bias that affects and alters user preferences. In order to visualize and compare the different biases, we will analyze the effects of recommendation systems and quantify the inequalities resulting from them.
