Multimedia Social Network modeling: a proposal

被引:21
作者
Amato, Flora [1 ]
Moscato, Vincenzo [1 ]
Picariello, Antonio [1 ]
Sperli, Giancarlo [1 ]
机构
[1] Univ Naples Federico II, Dipartimento Ingn Elettr & Tecnol Informaz, Via Claudio 21, I-80125 Naples, Italy
来源
2016 IEEE TENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC) | 2016年
关键词
D O I
10.1109/ICSC.2016.20
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In this paper we present a preliminary work concerning the definition of a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within a related environment. The proposed model is based on the hypergraph structure and allows us to represent in a simple way all the different kinds of relationships that are typical of a social network, in particular between multimedia content, between users and multimedia content and between users themselves, at the same time supporting several kinds of applications by means of the introduction of several ranking functions. In addition, we provide a strategy for mapping the proposed model into an object relational data model, for efficiently storing the related data. We also describe a first prototype that can store and integrate information from different OSNs (Facebook, Twitter) and multimedia sharing systems (Flickr, Lastfm), providing some facilities for browsing the entire hypergraph.
引用
收藏
页码:447 / 452
页数:6
相关论文
共 20 条
[1]
A Multimedia Recommender System [J].
Albanese, Massimiliano ;
d'Acierno, Antonio ;
Moscato, Vincenzo ;
Persia, Fabio ;
Picariello, Antonio .
ACM TRANSACTIONS ON INTERNET TECHNOLOGY, 2013, 13 (01)
[2]
Anandkumar Anima, 2015, ARXIV150304567
[3]
[Anonymous], 2003, PROC ACM SIGKDD INT
[4]
[Anonymous], 2012, P 20 ACM INT C MULT
[5]
[Anonymous], 2012, PROC 5 ACM INT C WEB
[6]
Bu J., 2010, P 18 ACM INT C MULT, P391, DOI [10.1145/ 1873951.1874005, DOI 10.1145/1873951.1874005]
[7]
A Graph-Based Consensus Maximization Approach for Combining Multiple Supervised and Unsupervised Models [J].
Gao, Jing ;
Liang, Feng ;
Fan, Wei ;
Sun, Yizhou ;
Han, Jiawei .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2013, 25 (01) :15-28
[8]
JI X, 2014, MULTIMED TOOLS APPL, P1
[9]
Reinforced Similarity Integration in Image-Rich Information Networks [J].
Jin, Xin ;
Luo, Jiebo ;
Yu, Jie ;
Wang, Gang ;
Joshi, Dhiraj ;
Han, Jiawei .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2013, 25 (02) :448-460
[10]
Li L., 2013, P 6 ACM INT C WEB SE, P305