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Multi-view Latent Learning Applied to Fashion Industry

DOI:10.1007/s10796-020-10005-8
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Giovanni Battista Gardino at GDP Analytics
Giovanni Battista Gardino
  • GDP Analytics
Rosa Meo at Università degli Studi di Torino
Rosa Meo
Giuseppe Craparotta
Giuseppe Craparotta
Giuseppe Craparotta
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Abstract and Figures

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.
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https://doi.org/10.1007/s10796-020-10005-8
Multi-view Latent Learning Applied to Fashion Industry
Giovanni Battista Gardino1·Rosa Meo2·Giuseppe Craparotta3
©Springer Science+Business Media, LLC, part of Springer Nature 2020
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.
Keywords Multi-view learning ·Latent spaces ·Fashion forecast
1 Introduction
Demand forecasting is one of the main challenges for
retailers and wholesalers in any industry. Proper demand
forecasting gives businesses valuable information about
potential profits in their current market so that managers
can take targeted decisions on pricing and business growth
strategies. If demand is underestimated, future sales can be
lost; on the other side, if suppliers are left with a surplus,
heavy markdowns strategies could be forced, implying
possible losses and cash flow issues.
In fashion industry, demand forecasting is more than
that: seasonal trends, lack of data and general uncertainty
make the prediction often difficult and unstable. A good
Rosa Meo
meo@di.unito.it
1GDP Analytics, Torino, Italy
2University of Torino, Torino, Italy
3EVO Pricing, Torino, Italy
fashion forecaster must take into account lots of variables,
such as political and economical system, demographics
of certain areas, consumer expectation, market trends,
internal business strategies and many others. In this context,
“forecasting” has two main goals: projecting past trends into
the future and anticipating forthcoming developments by
looking for signs of change.
The difficulty and uncertainty of the problem has caused
many debates over the last decades. On one hand people are
skeptical and believe that trends forecast may be regarded as
accurate because of its self-fulfilling process (see Adegeest
2016); on the other hand fashion analysts ensure that big
data and machine learning algorithms are becoming every
day more relevant (see Erhard and Bug 2016).
Recent researches by Na et al. (2013), Thomassey
(2010), and Thomassey (2014) show how advanced
analytical techniques have become an important part in
one of the most intuition-based and unpredictable indstry:
although, the use of big data cannot ever completely
redefine fashion industry as it is more an art than a
science, it definitely is revolutionizing the way industrialists
and brands produce apparels and accessories. Most of
Published online: 4 April 2020
Information Systems Frontiers (2021) 23:53–69
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... efficient data models, data processing pipelines and architectures to integrate standard and big data sources (Jovanovic et al. 2020) as well as to improve resource utilization and aggregate performance in shared environments (Michiardi et al. 2020); predictive analytics to forecast product demand in the fashion industry (Gardino et al. 2020) and techniques to deal with the lack of annotated data for sensor-based human activity recognition (Prabono et al. 2020); text data processing to assess the performance of text storage systems through a generic benchmark (Truicȃ et al. 2020) and innovative solutions to deal with specific use cases such as the legal domain (Bordino et al. 2020); novel approaches for mining social media to support intelligent transportation systems (Vallejos et al. 2020) and digging deep the IoT scenario (Ustek-Spilda et al. 2020); -solutions to deal with privacy issues in distance learning systems (Preuveneers et al. 2020). ...
... In this context, paper (Gardino et al. 2020) proposes a method for predicting product demand in the fashion industry. The proposed prediction method, called multi-VIew Bridge Estimation (VIBE), takes advantage of the existence of multiple views on items, i.e., sets of homogeneous features. ...
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