IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v23y2021i1d10.1007_s10796-020-10005-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-020-10005-8
    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/s10796-020-10005-8?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. 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.
    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. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.

    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. Giovanni Battista Gardino & Rosa Meo & Giuseppe Craparotta, 0. "Multi-view Latent Learning Applied to Fashion Industry," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    2. Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
    3. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    5. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    6. Chandadevi Giri & Yan Chen, 2022. "Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry," Forecasting, MDPI, vol. 4(2), pages 1-17, June.
    7. Rafael Teixeira & Mário Antunes & Diogo Gomes & Rui L. Aguiar, 2024. "Comparison of Semantic Similarity Models on Constrained Scenarios," Information Systems Frontiers, Springer, vol. 26(4), pages 1307-1330, August.
    8. Del Corso, Gianna M. & Romani, Francesco, 2019. "Adaptive nonnegative matrix factorization and measure comparisons for recommender systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 164-179.
    9. P Fogel & C Geissler & P Cotte & G Luta, 2022. "Applying separative non-negative matrix factorization to extra-financial data," Working Papers hal-03689774, HAL.
    10. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    11. Pakravan, Mohammad Reza & Kalashami, Mohammad Kavoosi, 2011. "Future prospects of Iran, U.S and Turkey's Pistachio exports," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 1(3), pages 1-8, September.
    12. Naiyang Guan & Lei Wei & Zhigang Luo & Dacheng Tao, 2013. "Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    13. Spelta, A. & Pecora, N. & Rovira Kaltwasser, P., 2019. "Identifying Systemically Important Banks: A temporal approach for macroprudential policies," Journal of Policy Modeling, Elsevier, vol. 41(1), pages 197-218.
    14. M. Moghadam & K. Aminian & M. Asghari & M. Parnianpour, 2013. "How well do the muscular synergies extracted via non-negative matrix factorisation explain the variation of torque at shoulder joint?," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 16(3), pages 291-301.
    15. Markovsky, Ivan & Niranjan, Mahesan, 2010. "Approximate low-rank factorization with structured factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3411-3420, December.
    16. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    17. Le Thi Khanh Hien & Duy Nhat Phan & Nicolas Gillis, 2022. "Inertial alternating direction method of multipliers for non-convex non-smooth optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 247-285, September.
    18. Zhaoyu Xing & Yang Wan & Juan Wen & Wei Zhong, 2024. "GOLFS: feature selection via combining both global and local information for high dimensional clustering," Computational Statistics, Springer, vol. 39(5), pages 2651-2675, July.
    19. Bertrand, Jean-Louis & Brusset, Xavier & Fortin, Maxime, 2015. "Assessing and hedging the cost of unseasonal weather: Case of the apparel sector," European Journal of Operational Research, Elsevier, vol. 244(1), pages 261-276.
    20. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.

    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:infosf:v:23:y:2021:i:1:d:10.1007_s10796-020-10005-8. 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.