Please use this identifier to cite or link to this item:
Title: Micro Tells Macro: Predicting the Popularity of Micro-Videos via a Transductive Model
Authors: Jingyuan Chen
Xuemeng Song
Liqiang Nie 
Xiang Wang 
Hanwang Zhang 
Tat-Seng Chua 
Keywords: Micro-videos
Multi-view learning
Popularity prediction
Issue Date: 15-Oct-2016
Publisher: Association for Computing Machinery, Inc
Citation: Jingyuan Chen, Xuemeng Song, Liqiang Nie, Xiang Wang, Hanwang Zhang, Tat-Seng Chua (2016-10-15). Micro Tells Macro: Predicting the Popularity of Micro-Videos via a Transductive Model. ACM Multimedia Conference 2016 : 898-907. ScholarBank@NUS Repository.
Abstract: Micro-videos, a new form of user generated contents (UGCs), are gaining increasing enthusiasm. Popular microvideos have enormous commercial potential in many ways, such as online marketing and brand tracking. In fact, the popularity prediction of traditional UGCs including tweets, web images, and long videos, has achieved good theoretical underpinnings and great practical success. However, little research has thus far been conducted to predict the popularity of the bite-sized videos. This task is nontrivial due to three reasons: 1) micro-videos are short in duration and of low quality; 2) they can be described by multiple heterogeneous channels, spanning from social, visual, acoustic to textual modalities; and 3) there are no available benchmark dataset and discriminant features that are suitable for this task. Towards this end, we present a transductive multi-modal learning model. The proposed model is designed to find the optimal latent common space, unifying and preserving information from different modalities, whereby micro-videos can be better represented. This latent space can be used to alleviate the information insufficiency problem caused by the brief nature of micro-videos. In addition, we built a benchmark dataset and extracted a rich set of popularity-oriented features to characterize the popular micro-videos. Extensive experiments have demonstrated the effectiveness of the proposed model. As a side contribution, we have released the dataset, codes and parameters to facilitate other researchers. © 2016 ACM.
Source Title: ACM Multimedia Conference 2016
ISBN: 9781450336031
DOI: 10.1145/2964284.2964314
Appears in Collections:Staff Publications
Students Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Micro Tells Macro.pdf6.71 MBAdobe PDF



Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.