Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph
Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g.,...
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sg-smu-ink.sis_research-26452013-01-10T07:09:08Z Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph NIU, Xiang LI, Lusong MEI, Tao SHEN, Jialie XU, Ke Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g., Flickr) by their unique parameters. In addition, because of the large scale of the network in Flickr, it is difficult to get all of the photos and the whole network. Thus, we are seeking for a method which can be used in such incomplete network. Inspired by a collaborative filtering method-Network-based Inference (NBI), we devise a weighted bipartite graph with undetected users and items to represent the resource allocation process in an incomplete network. Instead of image analysis, we propose a modified interdisciplinary models, called Incomplete Network-based Inference (INI). Using the data from 30 months in Flickr, we show the proposed INI is able to increase prediction accuracy by over 58.1%, compared with traditional NBI. We apply our proposed INI approach to personalized advertising application and show that it is more attractive than traditional Flickr advertising. 2012-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1646 info:doi/10.1109/ICME.2012.43 http://dx.doi.org/10.1109/ICME.2012.43 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Bipartite graph incomplete network inference personalized advertising popularity prediction social media Databases and Information Systems |
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Bipartite graph incomplete network inference personalized advertising popularity prediction social media Databases and Information Systems NIU, Xiang LI, Lusong MEI, Tao SHEN, Jialie XU, Ke Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
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Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g., Flickr) by their unique parameters. In addition, because of the large scale of the network in Flickr, it is difficult to get all of the photos and the whole network. Thus, we are seeking for a method which can be used in such incomplete network. Inspired by a collaborative filtering method-Network-based Inference (NBI), we devise a weighted bipartite graph with undetected users and items to represent the resource allocation process in an incomplete network. Instead of image analysis, we propose a modified interdisciplinary models, called Incomplete Network-based Inference (INI). Using the data from 30 months in Flickr, we show the proposed INI is able to increase prediction accuracy by over 58.1%, compared with traditional NBI. We apply our proposed INI approach to personalized advertising application and show that it is more attractive than traditional Flickr advertising. |
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NIU, Xiang LI, Lusong MEI, Tao SHEN, Jialie XU, Ke |
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NIU, Xiang LI, Lusong MEI, Tao SHEN, Jialie XU, Ke |
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NIU, Xiang |
title |
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
title_short |
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
title_full |
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
title_fullStr |
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
title_full_unstemmed |
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph |
title_sort |
predicting image popularity in an incomplete social media community by a weighted bi-partite graph |
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Institutional Knowledge at Singapore Management University |
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2012 |
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https://ink.library.smu.edu.sg/sis_research/1646 http://dx.doi.org/10.1109/ICME.2012.43 |
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