In just a little over half a century, the field of information retrieval has experienced spectacular growth and success, with IR applications such as search engines becoming a billion-dollar industry in the past decades. Recommender systems have seen an even more meteoric rise to success with wide-scale application by companies like Amazon, Facebook, and Netflix. But are search and recommendation really two different fields of research that address different problems with different sets of algorithms in papers published at distinct conferences?

In my talk, I want to argue that search and recommendation are more similar than they have been treated in the past decade. By looking more closely at the tasks and problems that search and recommendation try to solve, at the algorithms used to solve these problems and at the way their performance is evaluated, I want to show that there is no clear black and white division between the two. Instead, search and recommendation are part of a much more fluid continuum of methods and techniques for information access.