In our final chapter, we'll tackle one of the most ubiquitous problems prevalent in the e-commerce world, namely that of making effective product recommendations to customers. Recommendation systems, also referred to as recommender systems, often rely on the notion of similarity between objects, in an approach known as collaborative filtering. Its basic premise is that customers can be considered similar to each other if they share most of the products that they have purchased; equally, items can be considered similar to each other if they share a large number of customers who purchased them.

There are a number of different ways to quantify this notion of similarity, and we will present some of the commonly used alternatives. Whether we want to recommend movies, books, hotels, or restaurants, building a recommender system often involves dealing with very large data sets. Consequently, we'll introduce a few ideas and options for working with Big Data using...