...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Quality Control, Transactions >An Intelligent Video Tag Recommendation Method for Improving Video Popularity in Mobile Computing Environment
【24h】

An Intelligent Video Tag Recommendation Method for Improving Video Popularity in Mobile Computing Environment

机译：一种智能视频标签推荐方法，用于提高移动计算环境中的视频普及

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

Big data generated from social media and smart mobile devices has been regarded as a key to obtain insights into human behavior and been extensively utilized for launching marketing activities. A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this paper, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders, which is especially true since the screen is tight for most mobile devices. To this end, this paper proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology, the method extracts the entity name of video content as candidate keywords. Finally, a TF-SIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos. The experimental results show that the proposed method can effectively improve the social popularity of the videos as well as extend the length of video viewing time per playback.
机译：从社交媒体和智能移动设备生成的大数据被视为获得对人类行为的见解并被广泛用于发射营销活动的关键。成功的营销活动需要吸引对其内容的高社会普及，因为较高的普及通常表明影响力更强，更多的名气和更高的收入。在本文中，我们专注于如何提高在移动计算环境中像YouTube这样的网站上共享视频的普及的问题。显然，构成高质量的标题和标签是有益的，让观众发现他们的兴趣的视频，并增加他们观看更多视频的倾向。但是，对于上传者来说，这不是一件容易的任务，因为对于大多数移动设备来说，屏幕非常真实。为此，本文提出了一种基于多模态内容分析的新型混合方法，推荐视频上传器的关键字来撰写录音和视频，然后获得更高的普及。该方法通过对原始标签的文本语义分析的技术进行整合和视频内容的识别来生成候选关键字。一方面，将视频的原始关键字作为输入获取，该方法从三个顶级视频共享站点（YouTube，Yahoo Video，Bing视频）收集的WordNet和相关视频标题中的大多数相关单词。另一方面，通过识别具有深度学习技术的视频内容，该方法将视频内容的实体名称提取为候选关键字。最后，提出了一个TF-SIM算法来对候选关键字进行排名，并建议最相关的关键字上传，以优化其视频的标题和标签。实验结果表明，该方法可以有效提高视频的社会普及，并延长每次播放的视频观看时间的长度。

著录项

  • 来源
    《Quality Control, Transactions》 |2020年第2020期|6954-6967|共14页
  • 作者

    Zhou Renjie; Xia Dongchen; Wan Jian; Zhang Sanyuan;

  • 作者单位

    Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China|Hangzhou Dianzi Univ Key Lab Complex Syst Modeling & Simulat Minist Educ Hangzhou 310018 Peoples R China|Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China;

    Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China|Hangzhou Dianzi Univ Key Lab Complex Syst Modeling & Simulat Minist Educ Hangzhou 310018 Peoples R China;

    Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China|Hangzhou Dianzi Univ Key Lab Complex Syst Modeling & Simulat Minist Educ Hangzhou 310018 Peoples R China|Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou 310023 Peoples R China;

    Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310058 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Mobile computing; social media; big data; video tagging; video popularity; artificial intelligence; deep learning;

    机译：移动计算;社交媒体;大数据;视频标记;视频人气;人工智能;深度学习;

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