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Economic corollaries of personalized recommendations

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  • Molaie, Mir Majid
  • Lee, Wonjae

Abstract

The impact of recommendation systems (RSs) on the diversity of consumption is not transparent or well understood. Available studies, whether experimental or theoretical, show inconsistent and even opposite results, which manifests as debate in the literature. In this paper, we investigate the impact of two main recommender systems, neural collaborative filtering and deep content filtering, on sales diversity via a randomized field experiment. Our results confirm the capability of recommender engines in increasing or decreasing aggregate sales diversity. Nonetheless, they amplify homogenization and reduce individual-level consumption diversity. In conclusion, our research reconciles seemingly contradict previous findings and illustrates that the design of the RS is the decisive factor in homogenizing or diversifying product sales.

Suggested Citation

  • Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    • Handle: RePEc:eee:joreco:v:68:y:2022:i:c:s0969698922000960
    DOI: 10.1016/j.jretconser.2022.103003
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    1. Guo, Wenhao & Tian, Jin & Li, Minqiang, 2023. "Price-aware enhanced dynamic recommendation based on deep learning," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).

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