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

机译:探索类似YouTube的社交媒体上视频推荐的共享模式

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摘要

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.
机译:近年来,类似YouTube的视频共享网站(VSSes)越来越受欢迎。同时,类似Facebook的在线社交网络(OSN)在连接具有共同兴趣的人们方面取得了巨大的成功。现在,这两个新一代的网络服务之间的桥梁是,许多OSN用户与他们的朋友共享源自VSS的视频内容,并且已显示VSS视频的大部分视图都归因于这种社交网络共享方案。为了了解主要基于社交关系的视频共享行为如何影响用户的观看方式,我们分别与中国最大的在线社交网络和最大的视频共享网站RenRen和YouKu进行了长期测量。我们证明社交朋友具有更高的共同兴趣,他们的分享行为为增强推荐视频列表提供了指导。在本文中,我们迈出了学习OSN视频共享视频推荐模式的第一步。开发了自动编码器模型来学习不同视频在OSN中的共享方面的社会相似性。因此,我们提出了一种基于相似度的策略,以增强对类似YouTube的社交媒体的视频推荐。评估结果表明,与其他没有社会信息的广泛采用的策略相比,该策略可以显着提高建议的准确性和召回率。

著录项

  • 来源
    《Multimedia Systems》 |2014年第6期|675-691|共17页
  • 作者单位

    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;

    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;

    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;

    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada;

    Departmenf of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Video recommendation; Social media; Social similarity;

    机译:视频推荐;社交媒体;社会相似度;
  • 入库时间 2022-08-18 02:06:16

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