Talk Abstract: Neural network embeddings are often used in Natural Language Processing (NLP) to model words and sentences. The unsupervised “2vec” algorithms (e.g. word2vec) learn embeddings extremely quickly, scale well and can create beautiful visualisations. dunnhumby have recently extended these algorithms to work with grocery retail data; helping us to represent products, baskets and customers within the same multidimensional space. In this talk, I’ll explain how this has helped us to understand customer preferences better and discuss some of the potential use cases.
Bio: Adam is a Senior Data Scientist at dunnhumby, where he builds and deploys machine learning algorithms at scale. He is also a part-time Experimental Psychology PhD student at UCL, funded by dunnhumby. His research aims to better understand consumer purchase behaviour through a combination of data science, machine learning and cognitive modelling.