Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2012 IEEE International Conference on Multimedia and Expo |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 735-740 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-0-7695-4711-4 |
| ISBN (Print) | 978-1-4673-1659-0 |
| DOIs | |
| Publication status | Published - 5 Nov 2012 |
| Externally published | Yes |
| Event | 2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, Australia Duration: 9 Jul 2012 → 13 Jul 2012 |
Conference
| Conference | 2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 |
|---|---|
| Country | Australia |
| City | Melbourne, VIC |
| Period | 9/07/12 → 13/07/12 |
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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.
2012 IEEE International Conference on Multimedia and Expo. IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 735-740.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › Research › peer-review
TY - GEN
T1 - Predicting image popularity in an incomplete social media community by a weighted bi-partite graph
AU - Niu, Xiang
AU - Li, Lusong
AU - Mei, Tao
AU - Shen, Jialie
AU - Xu, Ke
PY - 2012/11/5
Y1 - 2012/11/5
N2 - 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.
AB - 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.
KW - Bipartite graph
KW - incomplete network inference
KW - personalized advertising
KW - popularity prediction
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=84868098787&partnerID=8YFLogxK
U2 - 10.1109/ICME.2012.43
DO - 10.1109/ICME.2012.43
M3 - Conference Paper published in Proceedings
SN - 978-1-4673-1659-0
SP - 735
EP - 740
BT - 2012 IEEE International Conference on Multimedia and Expo
PB - IEEE, Institute of Electrical and Electronics Engineers
ER -