图 书 馆
Twitter上的大规模学习Large Scale Learning at Twitter |
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| 课程网址: | http://videolectures.net/eswc2012_kolcz_twitter/ |
| 主讲教师: | Marko Grobelnik |
| 开课单位: | 约瑟夫·斯特凡学院 |
| 开课时间: | 2012-08-13 |
| 课程语种: | 英语 |
| 中文简介: |  Twitter代表着一个庞大而复杂的用户网络，其兴趣不断变化。讨论和互动的范围从很小的人群到很大的人群，其中大多数是在公众中进行的。兴趣是长期的还是短期的，由用户以及通过Twitter关注图生成的内容表示，即关注谁的内容。通过帮助用户与他人建立联系，查找相关信息和有趣的信息源，了解用户的兴趣对于提供良好的Twitter体验至关重要。信息在网络上的传播方式以及进行通信尝试的方式还可以帮助识别垃圾邮件发送者和其他服务滥用行为。由于大量的信息，快速的更改率和短消息的性质，了解用户及其偏好也是一个非常具有挑战性的问题。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，并创造了新的机会来更好地了解我们的用户。在演讲中，我将描述Twitter团队解决的许多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: | Twitter; 信息传播; 大规模学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-31：zyk |
| 最后编审: | 2021-04-09：yumf |
| 阅读次数: | 66 |