IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v24y2024i4d10.1007_s10660-022-09644-7.html
   My bibliography  Save this article

Product recommendation in internet business: an integrated approach of fuzzy sets and multiple attribute decision making

Author

Listed:
  • Niharika Gupta

    (O.P. Jindal Global University)

  • Harsh V. Verma

    (University of Delhi)

Abstract

Most of the present day recommender systems recommend the products to the buyers based on their personalized preferences and product information. However, these systems lack the ability to recognize and incorporate buyer’s cognitive and emotional characteristics. With an aim to address these limitations, the present study proposes a new methodology for online product recommendation based on the concepts of fuzzy sets. The proposed methodology takes into account multiple attributes of the products which are non-commensurable, conflicting and fuzzy in nature and recommends the most suitable product to the buyer on the basis of her/his desire on attribute satisfactions. The novelty of the methodology lies in its ability to derive and assimilate buyer’s cognitive characteristics such as tranquillity (anxiety), and attitudinal flexibility in the recommendation process as they are vital in choosing the right product as per buyer desire. The methodology specifically uses Maximal Entropy Ordered Weighted Averaging (MEOWA) operator to gather maximum information about the buyer while objectively deriving the parametric value β. Additionally, the methodology recognizes the buyers’ relative product preferences through a reorder vector which is subsequently used as indices for attribute aggregation using Induced Ordered Weighted Averaging (IOWA) operator. An algorithm Prod_Ranking() is written in our paper and implemented in “Python” to verify the robustness of our procedure. A numerical example of a car-purchasing problem is also illustrated to highlight the procedure developed. At the end, the proposed methodology is compared with other similar works and its advantages are well established.

Suggested Citation

  • Niharika Gupta & Harsh V. Verma, 2024. "Product recommendation in internet business: an integrated approach of fuzzy sets and multiple attribute decision making," Electronic Commerce Research, Springer, vol. 24(4), pages 2665-2691, December.
    • Handle: RePEc:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09644-7
    DOI: 10.1007/s10660-022-09644-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-022-09644-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-022-09644-7?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. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    2. Eunhye Kim & Christian Fernando Libaque-Saenz & Myeong-Cheol Park, 2019. "Understanding shopping routes of offline purchasers: selection of search-channels (online vs. offline) and search-platforms (mobile vs. PC) based on product types," Service Business, Springer;Pan-Pacific Business Association, vol. 13(2), pages 305-338, June.
    3. V. Kumar & Ashutosh Dixit & Rajshekar (Raj) G. Javalgi & Mayukh Dass, 2016. "Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing," Journal of the Academy of Marketing Science, Springer, vol. 44(1), pages 24-45, January.
    Full references (including those not matched with items on IDEAS)

    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. Neil A. Morgan & Kimberly A. Whitler & Hui Feng & Simos Chari, 2019. "Research in marketing strategy," Journal of the Academy of Marketing Science, Springer, vol. 47(1), pages 4-29, January.
    2. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    3. Thomas P. Novak & Donna L. Hoffman, 2019. "Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 216-237, March.
    4. Xin (Shane) Wang & Neil Bendle & Yinjie Pan, 2024. "Beyond text: Marketing strategy in a world turned upside down," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 939-954, July.
    5. Hsu, Sheila Hsuan-Yu & Tsou, Hung-Tai & Chen, Ja-Shen, 2021. "“Yes, we do. Why not use augmented reality?†customer responses to experiential presentations of AR-based applications," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    6. Surajit Bag & Shivam Gupta & Ajay Kumar & Uthayasankar Sivarajah, 2021. "An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance," Post-Print hal-03188195, HAL.
    7. Jian Liu & Xinyue Sun & Yanyan Liu, 2022. "Products pricing and return strategies for the dual channel retailers," Operational Research, Springer, vol. 22(4), pages 3841-3867, September.
    8. Kumar, V. & Ramachandran, Divya & Kumar, Binay, 2021. "Influence of new-age technologies on marketing: A research agenda," Journal of Business Research, Elsevier, vol. 125(C), pages 864-877.
    9. Zhu, Jingjie & Cheng, Mingming, 2024. "Automatic video analytics in tourism: A methodological review," Annals of Tourism Research, Elsevier, vol. 108(C).
    10. Jana Holthöwer & Jenny Doorn, 2023. "Robots do not judge: service robots can alleviate embarrassment in service encounters," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 767-784, July.
    11. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
    12. Oliver Schaer & Nikolaos Kourentzes & Robert Fildes, 2022. "Predictive competitive intelligence with prerelease online search traffic," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3823-3839, October.
    13. Marjeta Marolt & Hans-Dieter Zimmermann & Andreja Pucihar, 2020. "Enhancing Marketing Performance Through Enterprise-Initiated Customer Engagement," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    14. Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    15. Gandhi, Mohina & Kar, Arpan Kumar, 2022. "How do Fortune firms build a social presence on social media platforms? Insights from multi-modal analytics," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    16. Hyo Geun Song & Hyeon Jo, 2023. "Understanding the Continuance Intention of Omnichannel: Combining TAM and TPB," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    17. Benedict G. C. Dellaert, 2019. "The consumer production journey: marketing to consumers as co-producers in the sharing economy," Journal of the Academy of Marketing Science, Springer, vol. 47(2), pages 238-254, March.
    18. Peng, Leiqing & Luo, Mengting & Guo, Yulang, 2023. "Deposit AI as the “invisible hand†to make the resale easier: A moderated mediation model," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    19. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
    20. 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).

    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:spr:elcore:v:24:y:2024:i:4:d:10.1007_s10660-022-09644-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.