|
推特的大规模学习
Large Scale Learning at Twitter |
|
| |
| 课程网址: |
http://videolectures.net/eswc2012_kolcz_twitter/
|
| 主讲教师: |
Aleksander Kołcz; Marko Grobelnik
|
| 开课单位: |
推特公司
|
| 开课时间: |
2012-08-13
|
| 课程语种: |
英语
|
| 中文简介: |
Twitter代表了一个庞大而复杂的用户网络，用户兴趣不断变化。讨论和互动的范围从非常小到非常大，大多数都发生在公共场合。兴趣既有长期的也有短期的，通过用户生成的内容以及通过Twitter跟踪图（即谁跟踪谁的内容）来表达。了解用户的兴趣对于提供良好的Twitter体验至关重要，因为它可以帮助用户与他人建立联系，查找相关信息和有趣的信息源。信息在网络上传播的方式和通信尝试也有助于识别垃圾邮件发送者和其他服务滥用。了解用户及其偏好也是一个非常具有挑战性的问题，因为信息量非常大，变化速度很快，tweet的性质也很短。大规模的机器学习以及图形和文本挖掘一直在帮助我们解决这些问题，并创造新的机会来更好地了解我们的用户。在演讲中，我将描述Twitter团队解决的一些具有挑战性的建模问题，以及我们创建框架和基础架构以使大规模学习成为可能的方法。
|
| 课程简介: |
Twitter represents a large complex network of users with diverse and continuously evolving interests. Discussions and interactions range from very small to very large groups of people and most of them occur in the public. Interests are both long and short term and are expressed by the content generated by the users as well as via the Twitter follow graph, i.e. who is following whose content. Understanding user interests is crucial to providing good Twitter experience by helping users to connect to others, find relevant information and interesting information sources. The manner in which information is spread over the network and communication attempts are made can also help in identifying spammers and other service abuses. Understanding users and their preferences is also a very challenging problem due to the very large volume information, the fast rate of change and the short nature of the tweets. Large scale machine learning as well as graph and text mining have been helping us to tackle these problems and create new opportunities to better understand our users. In the talk I will describe a number of challenging modeling problems addressed by the Twitter team as well as our approach to creating frameworks and infrastructure to make learning at scale possible.
|
| 关 键 词: |
机器学习; 文本挖掘; 建模问题
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-09-28：heyf
|
| 阅读次数: |
41
|
|
图 书 馆