We describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, which is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.

We describe the Customer LifeTime Value (CLTV) prediction system deployed at ASOS.com, a global online fashion retailer. CLTV prediction is an important problem in e-commerce where an accurate estimate of future value allows retailers to effectively allocate marketing spend, identify and nurture high value customers and mitigate exposure to losses. The system at ASOS provides daily estimates of the future value of every customer and is one of the cornerstones of the personalised shopping experience. The state of the art in this domain uses large numbers of handcrafted features and ensemble regressors to forecast value, predict churn and evaluate customer loyalty. Recently, domains including language, vision and speech have shown dramatic advances by replacing handcrafted features with features that are learned automatically from data. We detail the system deployed at ASOS and show that learning feature representations is a promising extension to the state of the art in CLTV modelling. We propose a novel way to generate embeddings of customers, which addresses the issue of the ever changing product catalogue and obtain a significant improvement over an exhaustive set of handcrafted features.

Sensorimotor coupling is ubiquitous in living organisms. Sensory and motor systems are utterly useless if left without the presence of the other. One crucial faculty that organisms have developed with tremendous ecological advantages is the ability to discern between the origins of perceptual input as being originated by the environment or the organism itself, provided by resource efficient sensor and motor systems. This ability has been shown to be implemented through a specialized circuit (forward model) receiving a copy of the motor command (corollary discharge). We propose a fast method to derive a resource constrained forward model by framing sensorimotor coupling as a low-rank approximation of an overly detailed forward model. By framing the problem as a factorization approach we can resort to currently available off-the-shelf solvers for matrix factorization. We experimentally show that by solving the problem as a low-rank approximation we obtain more than an order of magnitude speed up relatively to minimizing the objective function with gradient descent methods. The development of resource constrained and ecologically adapted sensorimotor systems is essential for the deployment of low-cost energy efficient autonomous robots for the execution of specific tasks in particular environments.