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Automated Product Recommendations with Preference-Based Explanations

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  • Marchand, André
  • Marx, Paul

Abstract

Many online retailers, such as Amazon, use automated product recommender systems to encourage customer loyalty and cross-sell products. Despite significant improvements to the predictive accuracy of contemporary recommender system algorithms, they remain prone to errors. Erroneous recommendations pose potential threats to online retailers in particular, because they diminish customers’ trust in, acceptance of, satisfaction with, and loyalty to a recommender system. Explanations of the reasoning that lead to recommendations might mitigate these negative effects. That is, a recommendation algorithm ideally would provide both accurate recommendations and explanations of the reasoning for those recommendations. This article proposes a novel method to balance these concurrent objectives. The application of this method, using a combination of content-based and collaborative filtering, to two real-world data sets with more than 100 million product ratings reveals that the proposed method outperforms established recommender approaches in terms of predictive accuracy (more than five percent better than the Netflix Prize winner algorithm according to normalized root mean squared error) and its ability to provide actionable explanations, which is also an ethical requirement of artificial intelligence systems.

Suggested Citation

  • Marchand, André & Marx, Paul, 2020. "Automated Product Recommendations with Preference-Based Explanations," Journal of Retailing, Elsevier, vol. 96(3), pages 328-343.
  • Handle: RePEc:eee:jouret:v:96:y:2020:i:3:p:328-343
    DOI: 10.1016/j.jretai.2020.01.001
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Junhui & Balaji, M.S. & Luo, Jun & Jha, Subhash, 2022. "Effectiveness of product recommendation framing on online retail platforms," Journal of Business Research, Elsevier, vol. 153(C), pages 185-197.
    2. Yang, Defeng & Zhang, Jiaen & Sun, Yu & Huang, Zan, 2024. "Showing usage behavior or not? The effect of virtual influencers’ product usage behavior on consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Battisti, Sandro & Agarwal, Nivedita & Brem, Alexander, 2022. "Creating new tech entrepreneurs with digital platforms: Meta-organizations for shared value in data-driven retail ecosystems," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
    5. Martin Eling & Davide Nuessle & Julian Staubli, 2022. "The impact of artificial intelligence along the insurance value chain and on the insurability of risks," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(2), pages 205-241, April.
    6. Guyt, Jonne Y. & Datta, Hannes & Boegershausen, Johannes, 2024. "Unlocking the Potential of Web Data for Retailing Research," Journal of Retailing, Elsevier, vol. 100(1), pages 130-147.
    7. Huang, Ming-Hui & Rust, Roland T., 2022. "A Framework for Collaborative Artificial Intelligence in Marketing," Journal of Retailing, Elsevier, vol. 98(2), pages 209-223.
    8. Blut, Markus & Ghiassaleh, Arezou & Wang, Cheng, 2023. "Testing the performance of online recommendation agents: A meta-analysis," Journal of Retailing, Elsevier, vol. 99(3), pages 440-459.
    9. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    10. Liao, Shu-Hsien & Widowati, Retno & Hsieh, Yu-Chieh, 2021. "Investigating online social media users’ behaviors for social commerce recommendations," Technology in Society, Elsevier, vol. 66(C).

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