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Multi-view Latent Learning Applied to Fashion Industry

Author

Listed:
  • Giovanni Battista Gardino

    (GDP Analytics)

  • Rosa Meo

    (University of Torino)

  • Giuseppe Craparotta

    (EVO Pricing)

Abstract

Demand forecasting is one of the main challenges for retailers and wholesalers in any industry. Proper demand forecasting gives business valuable information about potential profits and helps managers in taking targeted decisions on business growth strategies. Nowadays almost all organizations use different data sources or databases for nearly every aspect of their operations so that the knowledge on products on sale belongs to several independent views. The methodology described in this paper addresses the issue of product demand forecasting in fashion industry exploiting a multi-view learning approach. In particular, we show how the integration and connection among multiple views improves results accuracy. In real-life applications not all the views are usually available before a product is put on the market but the utility of a proper demand forecasting increases if the prediction is available before the product launch. We show that missing views can be reconstructed by means of common latent factors; in particular, this paper presents a learning procedure that describes the connection between different views. This connection allows data integration from multiple sources and can be extended to the special case of partial data representation. The nearest neighbors in the latent space play a special role for this process and for a general improvement of the forecast quality. We experimented the proposed methodology on real fashion retail sales showing that multi-view latent learning provides a system that is able to reconstruct satisfactorily non yet available views and can be used to predict the volumes of sales well before the goods are put on the market.

Suggested Citation

  • Giovanni Battista Gardino & Rosa Meo & Giuseppe Craparotta, 2021. "Multi-view Latent Learning Applied to Fashion Industry," Information Systems Frontiers, Springer, vol. 23(1), pages 53-69, February.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:1:d:10.1007_s10796-020-10005-8
    DOI: 10.1007/s10796-020-10005-8
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    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Sébastien Thomassey, 2014. "Sales Forecasting in Apparel and Fashion Industry: A Review," Springer Books, in: Tsan-Ming Choi & Chi-Leung Hui & Yong Yu (ed.), Intelligent Fashion Forecasting Systems: Models and Applications, edition 127, chapter 0, pages 9-27, Springer.
    3. Na Liu & Shuyun Ren & Tsan-Ming Choi & Chi-Leung Hui & Sau-Fun Ng, 2013. "Sales Forecasting for Fashion Retailing Service Industry: A Review," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    4. Thomassey, Sébastien, 2010. "Sales forecasts in clothing industry: The key success factor of the supply chain management," International Journal of Production Economics, Elsevier, vol. 128(2), pages 470-483, December.
    5. Chu, Ching-Wu & Zhang, Guoqiang Peter, 2003. "A comparative study of linear and nonlinear models for aggregate retail sales forecasting," International Journal of Production Economics, Elsevier, vol. 86(3), pages 217-231, December.
    6. R Fildes & B Kingsman, 2011. "Incorporating demand uncertainty and forecast error in supply chain planning models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 483-500, March.
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    Cited by:

    1. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.

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