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 rep-utations with high rank correlation even under assumptions of extreme noise.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. NRF-2020R1A2B5B03001960), the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (NRF-2017M3C4A7069440), and by Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-01373, Artificial Intelligence Graduate School Program (Hanyang University)).
All Science Journal Classification (ASJC) codes
- Computer Science(all)