Recommender systems have become a popular and effective means to manage the ever-increasing amount of
multimedia content available today and to help users discover interesting new items. Today?s recommender
systems suggest items of various media types, including audio, text, visual (images), and videos. In fact,
scientific research related to the analysis of multimedia content has made possible effective content-based
recommender systems capable of suggesting items based on an analysis of the features extracted from the
item itself. The aim of this survey is to present a thorough review of the state of the art of recommender
systems that leverage multimedia content, by classifying the reviewed papers with respect to their media type,
the techniques employed to extract and represent their content features, and the recommendation algorithm.
Moreover, for each media type, we discuss various domains in which multimedia content plays a key role in
human decision making and is therefore considered in the recommendation process. Examples of the identified
domains include fashion, tourism, food, media streaming, and e-commerce.