IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v96y2020i3p328-343.html
   My bibliography  Save this article

Automated Product Recommendations with Preference-Based Explanations

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435920300014
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jretai.2020.01.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bleier, Alexander & Eisenbeiss, Maik, 2015. "The Importance of Trust for Personalized Online Advertising," Journal of Retailing, Elsevier, vol. 91(3), pages 390-409.
    2. Clement, Michel & Wu, Steven & Fischer, Marc, 2014. "Empirical generalizations of demand and supply dynamics for movies," International Journal of Research in Marketing, Elsevier, vol. 31(2), pages 207-223.
    3. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    4. Niladri B. Syam & Nanda Kumar, 2006. "On Customized Goods, Standard Goods, and Competition," Marketing Science, INFORMS, vol. 25(5), pages 525-537, September.
    5. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    6. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    7. Tuck Siong Chung & Roland T. Rust & Michel Wedel, 2009. "My Mobile Music: An Adaptive Personalization System for Digital Audio Players," Marketing Science, INFORMS, vol. 28(1), pages 52-68, 01-02.
    8. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    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. 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.
    8. 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.
    9. 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.
    10. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lena Bjørlo & Øystein Moen & Mark Pasquine, 2021. "The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    2. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    3. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    4. Nasim Mousavi & Panagiotis Adamopoulos & Jesse Bockstedt, 2023. "The Decoy Effect and Recommendation Systems," Information Systems Research, INFORMS, vol. 34(4), pages 1533-1553, December.
    5. Anuj Kumar & Kartik Hosanagar, 2019. "Measuring the Value of Recommendation Links on Product Demand," Information Systems Research, INFORMS, vol. 30(3), pages 819-838, September.
    6. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    7. Xuan Bi & Gediminas Adomavicius & William Li & Annie Qu, 2022. "Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1644-1660, May.
    8. Guy Aridor & Duarte Goncalves & Daniel Kluver & Ruoyan Kong & Joseph Konstan, 2022. "The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens," Papers 2211.14219, arXiv.org.
    9. Bruno J.D. Jacobs & Bas Donkers & Dennis Fok, 2016. "Model-Based Purchase Predictions for Large Assortments," Marketing Science, INFORMS, vol. 35(3), pages 389-404, May.
    10. Krafft, Manfred & Arden, Christine M. & Verhoef, Peter C., 2017. "Permission Marketing and Privacy Concerns — Why Do Customers (Not) Grant Permissions?," Journal of Interactive Marketing, Elsevier, vol. 39(C), pages 39-54.
    11. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    12. Abhijeet Ghoshal & Subodha Kumar & Vijay Mookerjee, 2020. "Dilemma of Data Sharing Alliance: When Do Competing Personalizing and Non‐Personalizing Firms Share Data," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1918-1936, August.
    13. Tobias Kretschmer & Christian Peukert, 2020. "Video Killed the Radio Star? Online Music Videos and Recorded Music Sales," Information Systems Research, INFORMS, vol. 31(3), pages 776-800, September.
    14. Marc Bourreau & Germain Gaudin, 2022. "Streaming platform and strategic recommendation bias," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(1), pages 25-47, February.
    15. Stefan F. Bernritter & Paul E. Ketelaar & Francesca Sotgiu, 2021. "Behaviorally targeted location-based mobile marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 677-702, July.
    16. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    17. Konstantin Bauman & Alexander Tuzhilin, 2022. "Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes," Information Systems Research, INFORMS, vol. 33(1), pages 179-202, March.
    18. Debora Dhanya Amarnath & Uma Pricilda Jaidev, 2021. "Toward an integrated model of consumer reactance: a literature analysis," Management Review Quarterly, Springer, vol. 71(1), pages 41-90, February.
    19. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.
    20. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jouret:v:96:y:2020:i:3:p:328-343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.