Optimizing online social networks for information propagation.
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 f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4015991?pdf=render |
| _version_ | 1818304440966316032 |
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| author | Duan-Bing Chen Guan-Nan Wang An Zeng Yan Fu Yi-Cheng Zhang |
| author_facet | Duan-Bing Chen Guan-Nan Wang An Zeng Yan Fu Yi-Cheng Zhang |
| author_sort | Duan-Bing Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T06:10:44Z |
| format | Article |
| id | doaj.art-5fb31e0668854eedad403c9243a174c2 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T06:10:44Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5fb31e0668854eedad403c9243a174c22022-12-21T23:57:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0195e9661410.1371/journal.pone.0096614Optimizing online social networks for information propagation.Duan-Bing ChenGuan-Nan WangAn ZengYan FuYi-Cheng ZhangOnline 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.http://europepmc.org/articles/PMC4015991?pdf=render |
| spellingShingle | Duan-Bing Chen Guan-Nan Wang An Zeng Yan Fu Yi-Cheng Zhang Optimizing online social networks for information propagation. PLoS ONE |
| title | Optimizing online social networks for information propagation. |
| title_full | Optimizing online social networks for information propagation. |
| title_fullStr | Optimizing online social networks for information propagation. |
| title_full_unstemmed | Optimizing online social networks for information propagation. |
| title_short | Optimizing online social networks for information propagation. |
| title_sort | optimizing online social networks for information propagation |
| url | http://europepmc.org/articles/PMC4015991?pdf=render |
| work_keys_str_mv | AT duanbingchen optimizingonlinesocialnetworksforinformationpropagation AT guannanwang optimizingonlinesocialnetworksforinformationpropagation AT anzeng optimizingonlinesocialnetworksforinformationpropagation AT yanfu optimizingonlinesocialnetworksforinformationpropagation AT yichengzhang optimizingonlinesocialnetworksforinformationpropagation |