Decoy Effect of Recommendation Systems on Real E-commerce Websites

Fan Mo, Tsuneo Matsumoto, Nao Fukushima, Fuyuko Kido, Hayato Yamana

Research output: Contribution to journalConference articlepeer-review


Recommendations on e-commerce websites help users discover their interests and assist them in deciding on items to purchase; however, users are prone to bias when comparing and selecting items due to cognitive limitations. The decoy effect, a common user bias phenomenon, has been confirmed in previous studies to induce user selection of items by adding one other item when comparing two items. Although previous studies have confirmed the difference in item selection with and without decoy items in controlled experiments, the mechanism of decoy effect in e-commerce websites has not been elucidated. This study is the first to propose a method for evaluating the decoy effect on real e-commerce websites. We proposed a row-based decoy effect detection method inspired by users' tendency to compare items in the same row when browsing recommended items on e-commerce websites. In addition, a new metric, called intra-row decoy effect rate, is proposed to evaluate the degree of decoy effect. Our month-long study of the recommended order of items on three e-commerce sites reveals that e-commerce sites influence users' item choices regardless of whether they intentionally generate a decoy effect.

Original languageEnglish
Pages (from-to)151-163
Number of pages13
JournalCEUR Workshop Proceedings
Publication statusPublished - 2022
Event9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2022 - Seattle, United States
Duration: 2022 Sept 22 → …


  • Decoy effect
  • asymmetric dominance effect
  • real e-commerce websites

ASJC Scopus subject areas

  • Computer Science(all)


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