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A framework for configuring collaborative filtering-based recommendations derived from purchase data

Author

Listed:
  • Stijn Geuens

    (IESEG - School of Management (LEM))

  • Kristof Coussement

    (IESEG - School of Management (LEM))

  • Koen W. de Bock

    (Audencia Recherche - Audencia Business School)

Abstract

No abstract is available for this item.

Suggested Citation

  • Stijn Geuens & Kristof Coussement & Koen W. de Bock, 2018. "A framework for configuring collaborative filtering-based recommendations derived from purchase data," Post-Print hal-01662029, HAL.
    • Handle: RePEc:hal:journl:hal-01662029
    DOI: 10.1016/j.ejor.2017.07.005
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    Citations

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    Cited by:

    1. Xiong, Yingqiu & Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Chai, Yidong & Ling, Haifeng, 2024. "Review-based recommendation under preference uncertainty: An asymmetric deep learning framework," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1044-1057.
    2. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    3. Hana Kim & Daeho Lee & Min Ho Ryu, 2018. "An Optimal Strategic Business Model for Small Businesses Using Online Platforms," Sustainability, MDPI, vol. 10(3), pages 1-11, February.
    4. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    5. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2021. "Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 389-409, June.
    6. K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.
    7. Behera, Rajat Kumar & Gunasekaran, Angappa & Gupta, Shivam & Kamboj, Shampy & Bala, Pradip Kumar, 2020. "Personalized digital marketing recommender engine," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    8. Liang Xiao & Qibei Lu & Feipeng Guo, 2020. "Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    9. Gupta, Mukul & Kumar, Pradeep, 2020. "Recommendation generation using personalized weight of meta-paths in heterogeneous information networks," European Journal of Operational Research, Elsevier, vol. 284(2), pages 660-674.
    10. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    11. T. Venkatesan & K. Saravanan & T. Ramkumar, 2019. "A Big Data Recommendation Engine Framework Based on Local Pattern Analytics Strategy for Mining Multi-Sourced Big Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-21, March.

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