Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping
This provides a solution for fashion retailers to recommend items to new customers or items with no sales history, though it is incremental as it builds on existing recommendation techniques.
The paper tackles the cold-start problem in fashion recommendation by introducing Fashion DNA, a method that uses deep neural networks to predict purchase likelihood from article information, achieving unbiased purchase probabilities without sales data.
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.