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    • 1. 发明申请
    • Automatic Video Recommendation
    • 自动视频推荐
    • US20090006368A1
    • 2009-01-01
    • US11771219
    • 2007-06-29
    • Tao MeiXian-Sheng HuaBo YangLinjun YangShipeng Li
    • Tao MeiXian-Sheng HuaBo YangLinjun YangShipeng Li
    • G06F17/30G06F3/00
    • H04N7/17318G06F16/735G06F16/78G06F16/7844G06F16/7847H04N21/466H04N21/4667H04N21/472
    • Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking.
    • 描述了自动视频推荐。 该建议不需要现有的用户配置文件。 源视频直接与用户选择的视频进行比较,以确定相关性,然后将其用作视频推荐的基础。 相对于包括至少一个基于内容的特征(例如视觉特征,听觉特征和内容导出的纹理特征)的加权特征集执行比较。 使用从视频提取的包括多模态特征(例如,视觉,听觉和纹理)的多模实现用于更可靠的相关性排名。 一个实施例使用基于一组预定义类别层次的自动文本分类生成的间接纹理特征。 另一个实施例使用基于用户点击历史的自学习来提高相关性排名。