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Quantifying the Effects of Recommendation Systems
February 04, 2020 Β· Declared Dead Β· π 2019 IEEE International Conference on Big Data (Big Data)
"No code URL or promise found in abstract"
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Authors
Sunshine Chong, AndrΓ©s Abeliuk
arXiv ID
2002.01077
Category
cs.IR: Information Retrieval
Cross-listed
cs.HC
Citations
6
Venue
2019 IEEE International Conference on Big Data (Big Data)
Last Checked
4 months ago
Abstract
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.
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