One of the fundamental promises of deep learning is its ability to build increasingly meaningful representations of data from complex but raw inputs. These techniques demonstrate remarkable efficacy on high dimensional data with unique proximity structures (image, natural language, graphs).

Not only are these types of data prevalent in financial services and e-commerce, but also they often capture extremely interesting aspects of social and economic behavior. For example, financial transactions and online purchases can be viewed as edges on graphs of economic activity. To date, these graphs are far less studied than social networks, though they provide a unique look at behavior, social structures, and risk. ​Meanwhile, activity or transaction sequences, usually determined by user sessions, can reflect the users’ long term and short term interests, which can be modeled by sequential models, and used to predict the user’s future activities. Although language models have been explored in session data modeling, how to re-use the representations learned from one job to another job effectively is still an open question.

Our goal is to bring together researchers from different domains to discuss the application of representation learning to financial services and e-commerce. For the first time, four major e-commerce companies (Amazon, Walmart, Alibaba and eBay) and two banks (JP Morgan and Capital One) have come together to organize this workshop along with researchers from academia. A shared goal across these industries and application areas is to transform large-scale representational data into tangible revenue for businesses. Towards this goal, our confirmed invited speakers will share diverse perspectives on ways that representation learning can be used to solve problems in financial services and e-commerce. This will also be a forum to share how research on financial services and e-commerce data provides unique insights into socio-economic behavior.