• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia, IEEE Transactions on >Connection Discovery Using Big Data of User-Shared Images in Social Media
【24h】

Connection Discovery Using Big Data of User-Shared Images in Social Media

机译：使用社交媒体中用户共享图像的大数据进行连接发现

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Billions of user-shared images are generated by individuals in many social networks today, and this particular form of user data is widely accessible to others due to the nature of online social sharing. When user social graphs are only accessible to exclusive parties, these user-shared images are proved to be an easier and effective alternative to discover user connections. This work investigated over 360   000 user shared images from two social networks, Skyrock and 163 Weibo, in which 3  million follower/ followee relationships are involved. It is observed that the shared images from users with a follower / followee relationship show relatively higher similarities . A multimedia big data system that utilizes this observed phenomenon is proposed as an alternative to user- generated tags and social graphs for follower/followee recommendation and gender identification. To the best of our knowledge, this is the first attempt in this field to prove and formulate such a phenomenon for mass user-shared images along with more practical prediction methods. These findings are useful for information or services recommendations in any social network with intensive image sharing, as well as for other interesting personalization applications, particularly when there is no access to those exclusive user social graphs.
机译：如今，数十亿用户共享图像是由许多社交网络中的个人生成的，由于在线社交共享的性质，这种特定形式的用户数据可被其他人广泛访问。当用户社交图只能由独占方访问时，这些用户共享的图像被证明是发现用户连接的更简单有效的替代方法。这项工作调查了来自两个社交网络Skyrock和163微博的360 000用户共享图像，其中涉及300万关注者/关注者关系。可以看出，具有追随者/追随者关系的用户共享的图像具有较高的相似性。提出了一种利用这种观察到的现象的多媒体大数据系统，作为用户生成的标签和社交图的替代者，以用于关注者/跟随者推荐和性别识别。据我们所知，这是该领域中首次尝试为大众用户共享的图像证明并提出这种现象以及更实用的预测方法。这些发现对于在具有密集图像共享的任何社交网络中的信息或服务建议以及其他有趣的个性化应用程序都非常有用，尤其是在无法访问那些专有用户社交图的情况下。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号