Reading: "Mining Large Streams of User Data for Personalized Recommendations"

Data Scientists across Skyscanner have started meeting every fortnight to discuss research papers that tackle similar problems to those that we face within Skyscanner. The 2nd paper we read was: "Mining Large Streams of User Data for Personalized Recommendations" (hi Xavier!). Just like the last post, we're we're also writing up a brief, non-technical overview the problems/opportunities we discussed. Netflix famously announced a 1M prize in 2006, calling on researchers across the world to improve their movie recommender system by 10%. To create this competition, they had to make a critical decision: how could Netflix measure a 10% improvement in their system?