Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph

Publication Type

Conference Proceeding Article

Publication Date

2012

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.

Keywords

Bipartite graph, incomplete network inference, personalized advertising, popularity prediction, social media

Discipline

Databases and Information Systems

Publication

Proceedings of the IEEE International Conference on Multimedia and Expo (ICME 2000)

First Page

735

Last Page

740

ISBN

9781467316590

Identifier

10.1109/ICME.2012.43

Publisher

IEEE

City or Country

Melbourne, Australia

Additional URL

http://dx.doi.org/10.1109/ICME.2012.43

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