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首页> 外文会议>2014 International Conference on Data Science and Advanced Analytics >Assessing the longevity of online videos: A new insight of a video's quality
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Assessing the longevity of online videos: A new insight of a video's quality

机译：评估在线视频的寿命：视频质量的新见解

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Recommending valuable videos to viewers is always crucial to an online video website and its related third parties. More particularly, what features and methods to be selected to assess the quality of online videos is still an on-going research topic. Unlike previous work attempted to evaluate a video only by its view count (a.k.a. popularity), this article proposes an additional scoring mechanism to capture a video's long-lasting value (a.k.a. longevity) to assist the judgment of its quality. Generally speaking, a longevous video tends to be watched frequently and therefore is considered to be more valuable. We introduce the concept of latent social impulses and the necessity of using them while measuring a video's longevity. When deriving latent social impulses, we view the video website as a digital signal filter and formulate the task as a least squares problem. The proposed longevity computation is based on the derived social impulses, and we use experiments to directly show that the computed longevity scores are able to capture overlooked information by popularity measure. Unfortunately, the required information to derive social impulses is not always public, which makes a third party unable to directly evaluate all videos' longevities. To solve this problem, we formulate a semi-supervised learning task by using part of videos having known longevity scores to predict the unknown longevity scores, and we propose a Gaussian Random Markov model with Loopy Belief Propagation to solve it. The conducted experiments on YouTube demonstrate that the proposed method can significantly improve the prediction results comparing to baseline models.
机译：向观看者推荐有价值的视频对于在线视频网站及其相关第三方始终至关重要。更具体地说，选择哪些功能和方法来评估在线视频的质量仍然是一个持续的研究主题。与以前的尝试仅通过视频观看次数（即受欢迎程度）评估视频的工作不同，本文提出了一种额外的评分机制来捕获视频的长期价值（也称为“长寿”）以帮助判断其质量。一般而言，长时间观看的视频往往会被观看，因此被认为更有价值。我们介绍了潜在的社会冲动的概念，以及在测量视频寿命的同时使用它们的必要性。在获得潜在的社会冲动时，我们将视频网站视为一个数字信号过滤器，并将任务表述为最小二乘问题。所提出的寿命计算是基于派生的社会冲动，我们使用实验直接表明，计算出的寿命得分能够通过受欢迎程度来捕获被忽略的信息。不幸的是，获得社会动力所需的信息并不总是公开的，这使得第三方无法直接评估所有视频的寿命。为了解决这个问题，我们通过使用部分寿命已知的视频来预测未知寿命的分数，来制定半监督学习任务，并提出了一种具有Loopy Belief Propagation的高斯随机马尔可夫模型来解决该问题。在YouTube上进行的实验表明，与基线模型相比，该方法可以显着改善预测结果。

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