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.
@article{arxiv.2002.01077,
title = {Quantifying the Effects of Recommendation Systems},
author = {Sunshine Chong and Andrés Abeliuk},
journal= {arXiv preprint arXiv:2002.01077},
year = {2020}
}
Comments
8 pages, 6 figures, accepted into the National Symposium of IEEE Big Data 2019