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Brand recommendations for cold-start problems using brand embeddings

Azcona, David orcid logoORCID: 0000-0003-3693-7906 and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2022) Brand recommendations for cold-start problems using brand embeddings. In: SAI Intelligent Systems Conference, 1-2 Sept 2022, Amsterdam, the Netherlands. ISBN 978-3-031-16074-5

Abstract
This paper presents our work to recommend brands to customers that might be relevant to their style but the brands are new to them. To promote the exploration and discovery of new brands, we leverage article-embeddings, also known as Fashion DNA, a learned en- coding for each article of clothing at Zalando, that is utilized for product and outfit recommendations. The model used in Fashion DNA’s work proposed a Logistic Matrix Factorization approach using sales data to learn customer style preferences. In this work, we evolved that approach to circumvent the cold-start problem for recommending new brands that do not have enough sales or digital footprint. First, we computed an embedding per brand, named Brand DNA, from the Fashion DNA of all articles that belong to a given brand. Then, we trained a model using Logistic Matrix Factorization to predict sales for a set of frequent customers and brands. That allowed us to learn customer style representations that can be leveraged to predict the likelihood of purchasing from a new brand by using its Brand DNA. Customers are also able to further explore Zalando’s assortment moving from the more popular products and brands.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Embeddings; Neural Networks; Latent Representations; Deep Learning
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Priceedings of Intelligent Systems and Applications. IntelliSys 2022, Arai, K. (eds). Lecture Notes in Networks and Systems. 544. Springer. ISBN 978-3-031-16074-5
Publisher:Springer
Official URL:https://doi.org/10.1007/978-3-031-16075-2_53
Copyright Information:© 2023 The Authors.
ID Code:27716
Deposited On:09 Sep 2022 12:16 by Alan Smeaton . Last Modified 09 Sep 2022 12:16
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