Name
Abstract | ||
|---|---|---|
AbstractSocial media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system-designed for big data applications-is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4018/IJMDEM.2018010103 | Periodicals |
Keywords | Field | DocType |
Big Data, Multimedia, Recommender System, Social Network, Social Media Contents | Recommender system,Computer vision,World Wide Web,Social media,Computer science,Artificial intelligence | Journal |
Volume | Issue | ISSN |
9 | 1 | 1947-8534 |
Citations | PageRank | References |
1 | 0.35 | 30 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giancarlo Sperli | 1 | 86 | 19.40 |
| Flora Amato | 2 | 458 | 66.48 |
| Fabio Mercorio | 3 | 144 | 23.07 |
| Mario Mezzanzanica | 4 | 64 | 18.34 |
| Vincenzo Moscato | 5 | 519 | 64.03 |
| Antonio Picariello | 6 | 858 | 87.40 |