The Barclays Data Science Hackathon: Building Retail Recommender Systems based on Customer Shopping Behaviour

From Data Science Milan meetup event:

In the depths of the last cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a one week hackathon where they collaboratively developed a recommendation system on top of Apache Spark. The contest consisted on using Bristol customer shopping behaviour data to make personalised recommendations in a sort of Kaggle-like competition where each team’s goal was to build an MVP and then repeatedly iterate on it using common interfaces defined by a specifically built framework.
The talk will cover:

• How to rapidly prototype in Spark (via the native Scala API) on your laptop and magically scale to a production cluster without huge re-engineering effort.

• The benefits of doing type-safe ETLs representing data in hybrid, and possibly nested, structures like case classes.

• Enhanced collaboration and fair performance comparison by sharing ad-hoc APIs plugged into a common evaluation framework.

• The co-existence of machine learning models available in MLlib and domain-specific bespoke algorithms implemented from scratch.

• A showcase of different families of recommender models (business-to-business similarity, customer-to-customer similarity, matrix factorisation, random forest and ensembling techniques).

• How Scala (and functional programming) helped our cause.