Online Social Space Identification. A Computational Tool for Optimizing Social Recommendations

被引:0
|
作者
Zoppis, Italo [1 ]
Trentini, Andrea [2 ]
Manzoni, Sara [1 ]
Micucci, Daniela [1 ]
Mauri, Giancarlo [1 ]
Pietrabissa, Giada [3 ,4 ]
Castelnuovo, Gianluca [3 ,4 ]
机构
[1] Univ Milano Bicocca, Dept Informat Syst & Commun, I-20126 Milan, Italy
[2] Univ Milan, Dept Comp Sci, I-20133 Milan, Italy
[3] Catholic Univ Milan, Dept Psychol, I-20123 Milan, Italy
[4] San Giuseppe Hosp, Ist Auxol Italiano IRCCS, Psychol Res Lab, I-28824 Verbania, Italy
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 09期
关键词
social information optimization; community identification; graphs; heuristic approximation; INFORMATION; NETWORKS; REQUIREMENTS; ALGORITHMS; TRUST;
D O I
10.3390/app10093024
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Conscious and functional use of online social spaces can support the elderly with mind cognitive impairment (MCI) in their daily routine, not only for systematic monitoring, but to achieve effective targeted engagement. In this sense, although social involvement can be obtained when elder's experiences, interests, and goals are shared and accepted by the community, an important subsistence for aging depends on the compelling information, users' co-operation, and resource reliability. Unfortunately, applications aimed at optimizing the information content and the reliability of online users are still missing. Within the SystEm of Nudge theory-based ICT applications for OldeR citizens (SENIOR) project, an advanced social platform will be created in which the elderly with MCI will be involved in "optimized" social communities, where suggestions for general well-being will be recognized as useful by users and shared by care providers. We report the results of our study addressing this issue from a theoretical perspective: we propose a computational problem and a heuristic solution where "expert users" can engage and support the elderly by suggesting available services and facilities for their conditions. The numerical experiments on synthetic data are of interest when considering large communities, which is the most natural situation for online social spaces.
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页数:14
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