图 书 馆
跨社交媒体网站连接用户：一种行为建模方法Connecting Users across Social Media Sites: A Behavioral-Modeling Approach |
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| 课程网址: | http://videolectures.net/kdd2013_zafarani_behavioral_modeling/ |
| 主讲教师: | Reza Zafarani |
| 开课单位: | 亚利桑那州立大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  人们出于各种目的使用各种社交媒体。单个站点上的信息通常不完整。当整合了补充信息的来源时，可以建立更好的用户档案来改善在线服务，例如验证在线信息。为了整合这些信息源，有必要在社交媒体网站上识别个人。本文旨在解决跨媒体用户识别问题。我们引入了一种方法（MOBIUS），用于在社交媒体网站上查找个人身份之间的映射。它由三个关键组件组成：第一个组件标识用户的独特行为模式，从而导致整个站点之间的信息冗余。第二部分构成利用这些行为模式利用信息冗余的功能；第三组件采用机器学习来有效地识别用户。我们正式定义了跨媒体用户识别问题，并显示MOBIUS在跨社交媒体站点识别用户方面是有效的。这项研究为跨社交媒体站点的分析和挖掘铺平了道路，并促进了跨站点创建新颖的在线服务。 |
| 课程简介: | People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better pro le of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identifi cation problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identiti es users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for e ffective user identi cation. We formally defi ne the cross-media user identi fication problem and show that MOBIUS is e ffective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites. |
| 关 键 词: | 用户识别; 机器学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-11：zyk |
| 最后编审: | 2020-11-11：zyk |
| 阅读次数: | 32 |