Machine Learning for the Internet Part 1 — Guest Editors’ Editorial

The World Wide Web has been at the center of a revolution in how algorithms are designed with massive amounts of data in mind. The essence of this revo- lution is conceptually very simple: real-world massive data sets are, more often than not, highly structured and regular. Regularities can be used in two com- plementary ways. First, systematic regularities within massive data sets can be used to craft algorithms that are potentially suboptimal in the worst-case, but highly effective for expected cases. Second, nonsystematic regularities—those that are too subtle to be encoded within an algorithm—can be discovered by automated methods so that the solutions are actually determined by the un- derlying data. In both cases, the existence of enormous problem instances that arise from a highly regular source is key to building more effective methods.