Abstract
Nowadays, multimedia data is surely one of the most popular and pervasive information and communication media that accompanies us in almost every walks of life. They allow fast and effective communication and sharing of information about peoples' lives, their behaviors, works, interests, and they are also the digital testimony of facts, objects, and locations and have become an essential component of Online Social Networks and represent the core element of Multimedia Streaming Platforms. One of the most challenging research topics concerning multimedia data is surely to provide users with recommendation facilities that are able to suggest content of interest within very large collections of data. Recently, traditional recommendation strategies have been extended to handle multimedia data and the related features giving rise to the so-called Multimedia Recommender Systems. In this presentation, we will discuss their foundations and the different types of approaches that characterize them, referring to some of the research works that have distinguished the main literature in the field over the past decade. Finally, future lines of research and main open challenges will be identified ,by looking at the problems associated with the ever-increasing volume and complex nature of multimedia data that must be analyzed.