  The task of item recommendation is to select the best items for a user from a
large catalogue of items. Item recommenders are commonly trained from implicit
feedback which consists of past actions that are positive only. Core challenges
of item recommendation are (1) how to formulate a training objective from
implicit feedback and (2) how to efficiently train models over a large item
catalogue. This article provides an overview of item recommendation, its unique
characteristics and some common approaches. It starts with an introduction to
the problem and discusses different training objectives. The main body deals
with learning algorithms and presents sampling based algorithms for general
recommenders and more efficient algorithms for dot product models. Finally, the
application of item recommenders for retrieval tasks is discussed.
