Geodesic
A Robust Reputation System using Online Reviews
Computer Science and Information Systems, Tome 17 (2020) no. 2.

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

Evaluating sellers in an online marketplace is an important yet non-trivial task. Many online platforms such as eBay and Amazon rely on buyer reviews to estimate the reliability of sellers on their platform. Such reviews are, however, often biased by: (1) intentional attacks from malicious users and (2) conflation between a buyer’s perception of seller performance and item satisfaction. Here, we present a novel approach to mitigating these issues by decoupling measures of seller performance and item quality, while reducing the impact of malignant reviews. An extensive simulation study shows that our proposed method can recover seller reputations with high rank correlation even under assumptions of extreme noise.
Keywords: reputation, reviews, attacks 
@article{CSIS_2020_17_2_a7,
     author = {Hyun-Kyo Oh and Jongbin Jung and Sunju Park and Sang-Wook Kim},
     title = {A {Robust} {Reputation} {System} using {Online} {Reviews}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {17},
     number = {2},
     year = {2020},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a7/}
}
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Hyun-Kyo Oh; Jongbin Jung; Sunju Park; Sang-Wook Kim. A Robust Reputation System using Online Reviews. Computer Science and Information Systems, Tome 17 (2020) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2020_17_2_a7/