图 书 馆
学习的相似性度量的社会媒体事件识别Learning Similarity Metrics for Event Identification in Social Media |
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| 课程网址: | http://videolectures.net/wsdm2010_becker_lsmf/ |
| 主讲教师: | Hila Becker |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2010-10-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Social media sites (e.g., Flickr, YouTube, and Facebook) are a popular distribution outlet for users looking to share their experiences and interests on the Web. These sites host substantial amounts of user-contributed materials (e.g., photographs, videos, and textual content) for a wide variety of real-world events of different type and scale. By automatically identifying these events and their associated user-contributed social media documents, which is the focus of this paper, we can enable event browsing and search in state-of-the-art search engines. To address this problem, we exploit the rich “context” associated with social media con- tent, including user-provided annotations (e.g., title, tags) and automatically generated information (e.g., content creation time). Using this rich context, which includes both textual and non-textual features, we can define appropriate document similarity metrics to enable online clustering of media to events. As a key contribution of this paper, we explore a variety of techniques for learning multi-feature similarity metrics for social media documents in a principled manner. We evaluate our techniques on large-scale, real- world datasets of event images from Flickr. Our evaluation results suggest that our approach identifies events, and their associated social media documents, more effectively than the state-of-the-art strategies on which we build. |
| 关 键 词: | 社会媒体网站; 材料; 自动识别; 事件浏览; 搜索引擎 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-11-13：yumf |
| 阅读次数: | 76 |