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Contextual Video Recommendation by Multimodal Relevance and User Feedback

机译：通过多模式关联和用户反馈进行上下文视频推荐

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摘要

With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.
机译：随着互联网上视频内容的传递达到前所未有的水平，视频推荐已成为最普及的在线视频服务之一，视频推荐根据目标用户的历史和当前观看或偏好向他们推荐相关视频。本文介绍了一种基于多模式内容相关性和用户反馈的新型上下文视频推荐系统，称为VideoReach。我们认为在线视频通常由不同的方式组成（即，视觉和音频轨道以及相关的文本，例如查询，关键字和周围的文本）。因此，就多模式相关性而言，推荐的视频应与当前观看相关。我们还认为视频的不同部分对用户的兴趣程度不同，并且不同的功能和方式对整体相关性的贡献也不同。结果，在用户反馈（即用户点击率）方面，推荐视频也应与当前用户相关。然后，我们为VideoReach设计一个统一的框架，该框架可以通过相关性反馈和注意力融合来无缝集成多模式相关性和用户反馈。 VideoReach代表了由视频内容和用户点击驱动的上下文推荐的首批尝试之一，而没有假设有足够的可用用户配置文件集合。我们对大规模的真实视频数据进行了实验，并报告了VideoReach的有效性。

著录项

  • 来源
    《ACM Transactions on Information Systems》 |2011年第2期|p.101-124|共24页
  • 作者

    TAO MEI; BO YANG; XIAN-SHENG HUA; SHIPENG LI;

  • 作者单位

    Microsoft Research Asia, Building 2,No. 5 Dan Ling Street, Haidian District,Beijing, 100080 P. R. China;

    Institute for Robotics and Intelligent Systems, University of Southern California, Los Angeles, CA 90089;

    Microsoft Research Asia, Building 2,No. 5 Dan Ling Street, Haidian District,Beijing, 100080 P. R. China;

    Microsoft Research Asia, Building 2,No. 5 Dan Ling Street, Haidian District,Beijing, 100080 P. R.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; image retrieval; relevance feedback;

    机译：视频推荐;图像检索;相关性反馈;

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