Back to Search
Start Over
Optimizing Online Social Networks for Information Propagation
- Source :
- PLoS ONE, PLoS ONE, Vol 9, Iss 5, p e96614 (2014)
- Publication Year :
- 2014
- Publisher :
- Public Library of Science, 2014.
-
Abstract
- Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these systems in which the connections in the online social networks are optimized for the information propagation so that users can receive interesting news or stories from their leaders. Validation of such adaptive social recommendation methods in the literature assumes uniform distribution of users' activity frequency. In this paper, our empirical analysis shows that the distribution of online users' activity is actually heterogenous. Accordingly, we propose a more realistic multi-agent model in which users' activity frequency are drawn from a power-law distribution. We find that previous social recommendation methods lead to serious delay of information propagation since many users are connected to inactive leaders. To solve this problem, we design a new similarity measure which takes into account users' activity frequencies. With this similarity measure, the average delay is significantly shortened and the recommendation accuracy is largely improved.
- Subjects :
- Information propagation
Computer and Information Sciences
Uniform distribution (continuous)
Informatics
lcsh:Medicine
Similarity measure
Recommender system
Bioinformatics
Social Networking
Medicine
Humans
Computer Simulation
lcsh:Science
Social influence
Numerical Analysis
Internet
Multidisciplinary
Information retrieval
business.industry
Information Dissemination
Physics
lcsh:R
Reproducibility of Results
Models, Theoretical
Internet Standard
Information overload
Social research
Physical Sciences
Interdisciplinary Physics
lcsh:Q
business
Mathematics
Algorithms
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....1ee00b6f2cb39def950a82e8ff703b0e