This video is part of the Deep Learning in Retail & Advertising Summit, London, 2018 Event. If you would like to access all of the videos please click here.

Offline applications of Recommender Systems at Royal Mail

Whilst recommender systems are regularly found in the context of digital marketing and campaign selections, these powerful algorithms have the flexibility to be applied to a wide range of business problems. At Royal Mail we have used a hybrid recommendation system to better target our B2B marketing communications and are currently exploring the possibility of using recommenders to aid decision making by a wide range of colleagues. In this talk I will cover the challenges of moving recommenders away from the traditional spaces they occupy and will discuss the power of combining business knowledge and recommendation algorithms to enable data driven decision making in an operational setting.

Kat James , Senior Data Scientist at Royal Mail

Kat James received her PhD from the University of Oxford in Statistical Genomics and completed a short Post Doc in Kumamoto, Japan working on the statistical challenges presented by whole blood RNASeq sampling in HIV-2 infected patients. Following on from roles at British Airways building solutions to destination recommendation problems and at Aviva, applying NLP techniques to customer complaints data, she is currently a Senior Data Scientist at Royal Mail, working on a variety of Data Science applications around optimisation, IoT devices, marketing and data-driven decision making. Current interests include recommender systems, AI, IoT and NLP.