Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping

TL;DR

This paper introduces Fashion DNA, a neural network-based method that maps fashion items into an abstract space using article data to predict sales and recommend items, effectively addressing cold-start issues.

Contribution

The paper presents a novel deep learning approach to derive Fashion DNA vectors from article information, enabling unbiased recommendations and item similarity measures without relying solely on sales data.

Findings

Models accurately predict purchase likelihood based on article data.

Fashion DNA vectors effectively measure item similarity.

Approach overcomes cold-start problem in fashion recommendation systems.

Abstract

We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space. Our approach is based on a deep neural network architecture that ingests curated article information such as tags and images, and is trained to predict sales for a large set of frequent customers. In the process, a dual space of customer style preferences naturally arises. Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity. Importantly, our models are able to generate unbiased purchase probabilities for fashion items based solely on article information, even in absence of sales data, thus circumventing the "cold-start problem" of collaborative recommendation approaches. Likewise, it generalizes easily and reliably to customers outside the training set. We experiment with Fashion DNA models based on visual and/or tag item data, evaluate their recommendation power, and discuss the resulting article similarities.