Using ‘basket complementarity’ to make product recommendations
Purchase suggestions – e.g., “if you are buying that, you might also want this” – are, to a large extent, informed by the concept of complementarity: that certain products are often bought and/or used together. A journal paper by Puka and Jedrusik sheds light on how these product recommendations can be derived, as Moinak Bhaduri explains.
Pulling patterns out of data with a graph
Large volumes of data are pouring in every day from scientific experiments, so much so that it is now commonplace to perform dimension reduction in order to reduce a large number of measurements to a set of key values that are easier to visualize and interpret. Enter ‘The Sequencer’, a proposed method to find trends within high-dimensional datasets.
Determining the best way to route drivers for ridesharing via reinforcement learning
A/B testing is often used to evaluate the impact of design ‘treatments’ — for example, are people who see advert A more likely to buy something than those who see advert B? Classical methods typically assume that changing one person’s treatment will not affect others, but what if that’s not the case? A paper by Shi et al. aims to address this problem.