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A Video-Based Automated Recommender (VAR) System for Garments

Citations

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Cited by:

  1. 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.
  2. Li, Yaqiu & Meg Lee, Hsin Hsuan & Blasco-Arcas, Lorena, 2025. "Computer vision in branding: A conceptual framework and future research agenda," Journal of Business Research, Elsevier, vol. 193(C).
  3. Ratchford, Brian & Soysal, Gonca & Zentner, Alejandro & Gauri, Dinesh K., 2022. "Online and offline retailing: What we know and directions for future research," Journal of Retailing, Elsevier, vol. 98(1), pages 152-177.
  4. Sebastian Gabel & Artem Timoshenko, 2022. "Product Choice with Large Assortments: A Scalable Deep-Learning Model," Management Science, INFORMS, vol. 68(3), pages 1808-1827, March.
  5. Feng, Cong & Fay, Scott, 2022. "An empirical investigation of forward-looking retailer performance using parking lot traffic data derived from satellite imagery," Journal of Retailing, Elsevier, vol. 98(4), pages 633-646.
  6. 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.
  7. Linda Hagen & Kosuke Uetake & Nathan Yang & Bryan Bollinger & Allison J. B. Chaney & Daria Dzyabura & Jordan Etkin & Avi Goldfarb & Liu Liu & K. Sudhir & Yanwen Wang & James R. Wright & Ying Zhu, 2020. "How can machine learning aid behavioral marketing research?," Marketing Letters, Springer, vol. 31(4), pages 361-370, December.
  8. Dellaert, B.G.C. & Baker, T. & Johnson, E.J., 2017. "Partitioning Sorted Sets: Overcoming Choice Overload while Maintaining Decision Quality," ERIM Report Series Research in Management 18-2, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  9. 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.
  10. Ye Hu & Kitty Wang & Ming Chen & Sam Hui, 2021. "Herding Among Retail Shoppers: the Case of Television Shopping Network," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(1), pages 27-40, June.
  11. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
  12. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
  13. Garaus, Marion & Wagner, Udo & Rainer, Ricarda C., 2021. "Emotional targeting using digital signage systems and facial recognition at the point-of-sale," Journal of Business Research, Elsevier, vol. 131(C), pages 747-762.
  14. Liu, Weihua & Yan, Xiaoyu & Li, Xiang & Wei, Wanying, 2020. "The impacts of market size and data-driven marketing on the sales mode selection in an Internet platform based supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
  15. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
  16. Schwenzow, Jasper & Hartmann, Jochen & Schikowsky, Amos & Heitmann, Mark, 2021. "Understanding videos at scale: How to extract insights for business research," Journal of Business Research, Elsevier, vol. 123(C), pages 367-379.
  17. Li Xiao & D. J. Wu & Min Ding, 2024. "A Smart Ad Display System," Information Systems Research, INFORMS, vol. 35(4), pages 1873-1889, December.
  18. Schraml, Christopher, 2025. "Automated Video Analytics in Marketing Research: A Systematic Literature Review and a Novel Multimodal Large Language Model Method," OSF Preprints 63nbc_v1, Center for Open Science.
  19. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
  20. de Haan, Evert & Padigar, Manjunath & El Kihal, Siham & Kübler, Raoul & Wieringa, Jaap E., 2024. "Unstructured data research in business: Toward a structured approach," Journal of Business Research, Elsevier, vol. 177(C).
  21. Daria Dzyabura & John R. Hauser, 2019. "Recommending Products When Consumers Learn Their Preference Weights," Marketing Science, INFORMS, vol. 38(3), pages 417-441, May.
  22. W. Jason Choi & Qihong Liu & Jiwoong Shin, 2024. "Predictive Analytics and Ship-Then-Shop Subscription," Management Science, INFORMS, vol. 70(2), pages 1012-1028, February.
  23. Yue Guan & Benjiang Lu & Wei Yan & Guoqing Chen, 2025. "Show me your face: investigating the effect of facial features in review images on review helpfulness," Electronic Commerce Research, Springer, vol. 25(1), pages 529-551, February.
  24. Merfeld, Katrin & Klein, Jan F. & de Regt, Anouk & Baltin (née Riegger), Anne-Sophie & Henkel, Sven, 2025. "In-store technology personalization: A typology and research agenda based on type of automation and data collection," Journal of Business Research, Elsevier, vol. 191(C).
  25. Lanfei Shi & Jin Liu & Yongjun Li & Natasha Zhang Foutz, 2025. "Ephemeral State-Dependent Recommendation for Digital Content," Information Systems Research, INFORMS, vol. 36(4), pages 2344-2357, December.
  26. Pradeep Chintagunta & Dominique M. Hanssens & John R. Hauser, 2016. "Editorial—Marketing Science and Big Data," Marketing Science, INFORMS, vol. 35(3), pages 341-342, May.
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