图 书 馆
论社会邻里利益推断的质量On the Quality of Inferring Interests From Social Neighbors |
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| 课程网址: | http://videolectures.net/kdd2010_wen_oqiifs/ |
| 主讲教师: | Zhen Wen |
| 开课单位: | IBM公司 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his/her social connections -- friends, friends' friends, 3-degree friends, etc? Is ``Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such ``informal networks", we investigated the ``formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8, and inference quality of explicit interests by up to 101. |
| 关 键 词: | 社交关系; 隐私保护; 正式网络 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-04-30：chenxin |
| 阅读次数: | 51 |