  We increasingly depend on a variety of data-driven algorithmic systems to
assist us in many aspects of life. Search engines and recommender systems
amongst others are used as sources of information and to help us in making all
sort of decisions from selecting restaurants and books, to choosing friends and
careers. This has given rise to important concerns regarding the fairness of
such systems. In this work, we aim at presenting a toolkit of definitions,
models and methods used for ensuring fairness in rankings and recommendations.
Our objectives are three-fold: (a) to provide a solid framework on a novel,
quickly evolving, and impactful domain, (b) to present related methods and put
them into perspective, and (c) to highlight open challenges and research paths
for future work.
