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Multi-view Latent Learning Applied to Fashion Industry
Information Systems Frontiers ( IF 5.9 ) Pub Date : 2020-04-04 , DOI: 10.1007/s10796-020-10005-8
Giovanni Battista Gardino , Rosa Meo , Giuseppe Craparotta

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.



中文翻译:

多视角潜在学习在时装行业中的应用

需求预测是任何行业的零售商和批发商面临的主要挑战之一。正确的需求预测可以为业务提供有关潜在利润的有价值的信息,并帮助管理人员针对业务增长策略做出有针对性的决策。如今,几乎所有组织都在其运营的几乎每个方面都使用不同的数据源或数据库,因此,有关销售产品的知识属于几种独立的观点。本文介绍的方法论通过多视图学习方法解决了时尚行业中产品需求预测的问题。特别是,我们展示了多个视图之间的集成和连接如何提高结果的准确性。在实际应用中,并非所有视图通常都在产品投放市场之前可用,但是如果可以在产品发布之前获得适当的需求预测,那么实用需求预测的实用性就会增加。我们表明,缺失的观点可以通过共同的潜在因素来重建。特别是,本文提出了一种学习过程,该过程描述了不同视图之间的联系。这种连接允许从多个来源进行数据集成,并且可以扩展到部分数据表示的特殊情况。潜在空间中最近的邻居在此过程中以及在总体上提高了预测质量方面起着特殊的作用。

更新日期:2020-04-21
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