Multimedia Recommender Systems: Approaches and Challenges

Multimedia Recommender Systems: Approaches and Challenges


18 December 2024
VIDEO

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.

Speakers

  • Vincenzo Moscato
    Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione - Università degli Studi di Napoli "Federico II"
    HOMEPAGE

    Vincenzo Moscato is a Full Professor at the Electrical Engineering and Information Technology Department of University of Naples Federico II, where he is the owner of "Database Systems" and "Big Data Engineering" teachings for the bachelor and master’s degree programs in Computer Engineering, respectively. Currently, he is the leader of PICUS (Pattern and Intelligence Computation for mUltimedia Systems) departmental research group, and the Scientific Coordinator for the University of Naples unit of the Data Science National Laboratory of CINI (National Research Consortium on Computer Science). In addition, he is also the director of CINI's national ITEM laboratory. His current research activities lay in the area of Big Data Analytics, Aritificial Intelligence, Multimedia Social Network Analysis and Multimedia Recommender Systems. In addition, he is the Co-founder of the Academic Spin-off Data JAM srl, and won an International Award by Oracle Corporation for the "Knowledge graphs for next-generation health science applications" project. He was in the Program Committee (PC) of a plethora of international and top-ranked conferences, and the PC chair of a dozen of IEEE/ACM international conferences. He served as reviewer in numerous international journals, including some of the most important journals concerning Multimedia, Knowledge and Data Engineering and Artificial Intelligence topics, and currently he is in the editorial boards of several international journals, including, among others, "Expert System and Applications" , "Intelligent Information Systems" and "IEEE Transaction on Neural Networks and Learning Systems". Finally, he was an author of more than 200 publications in international journals, conference proceedings and book chapters. About 80 of such publications are available on top-ranked journals (Q1 and Q2 from SCIMAGO ranking) or included in Proceedings of top-ranked conferences.