2020/02/04 by Sunshine Chong, Andrés Abeliuk, Chong, Sunshine +1 · 1 citation
Business, Management and Accounting · Computer Science · Psychology · #Business #Collaborative filtering #Computer science #Consumer Market Behavior and Pricing #Data science #Feedback loop #Human–computer interaction #Information retrieval #Mobile Crowdsensing and Crowdsourcing #Order (exchange) #Perception #Psychology #Recommender Systems and Techniques #Recommender system #World Wide Web #cs.HC #cs.IR
paper · pdf · doi:10.48550/arxiv.2002.01077
published in arXiv (Cornell University) (Cornell University) · 8 pages, 6 figures, accepted into the National Symposium of IEEE Big Data 2019
arxiv created 2020/02/04 · openalex publication_date 2020/02/04 · arxiv updated 2020/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.