In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising results.
Recommendation in Social Media Networks / Amato, Flora; Moscato, Vincenzo; Picariello, Antonio; Sperlì, Giancarlo. - Article number 7966745(2017), pp. 213-216. (Intervento presentato al convegno IEEE 3rd International Conference on Multimedia Big Data, BigMM 2017 tenutosi a Laguna Hills, CA, United States nel 19 April - 21 April 2017) [10.1109/BigMM.2017.55].
Recommendation in Social Media Networks
Flora Amato;Vincenzo Moscato;Antonio Picariello;Giancarlo Sperlì
2017
Abstract
In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
