Abstract
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.
Citation
Yashar Deldjoo,
Markus
Schedl,
Paolo Cremonesi,
Gabriella Pasi
Recommender Systems Leveraging Multimedia Content
ACM Computing Surveys,
53(5): doi:10.1145/3407190, 2020.
BibTeX
@article{Deldjoo2020RSLeveraging,
title = {Recommender Systems Leveraging Multimedia Content},
author = {Deldjoo, Yashar and Schedl, Markus and Cremonesi, Paolo and Pasi, Gabriella},
journal = {ACM Computing Surveys},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3407190},
url = {https://doi.org/10.1145/3407190},
volume = {53},
number = {5},
month = {sep},
articleno = {106},
numpages = {38},
year = {2020}
}