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Multimedia Systems
Erschienen in: Multimedia Systems 6/2014

01.11.2014 | Regular Paper

Exploring sharing patterns for video recommendation on YouTube-like social media

verfasst von: Xiaoqiang Ma, Haiyang Wang, Haitao Li, Jiangchuan Liu, Hongbo Jiang

Erschienen in: Multimedia Systems | Ausgabe 6/2014

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Abstract

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.
Vorheriger Artikel Grassmann multimodal implicit feature selection
Nächster Artikel Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

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Fußnoten
1
The RenRen engineers are also working on the behaviors of these highly active share users to see if they are some "OSN bots" on the user clients. However, the detailed discussion of this problem is beyond the scope of this paper.
 
2
In fact, the exact number equals the length of the longest video list, which is 240, minus the number of recommendation seeds.
 
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Metadaten
Titel
Exploring sharing patterns for video recommendation on YouTube-like social media
verfasst von
Xiaoqiang Ma
Haiyang Wang
Haitao Li
Jiangchuan Liu
Hongbo Jiang
Publikationsdatum
01.11.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 6/2014
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-013-0309-1

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