Eventera:海量异构在线媒体实时事件推荐系统

Dongyeop Kang, Donggyun Han, Nahea Park, Sangtae Kim, U. Kang, Soobin Lee
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引用次数: 9

摘要

面对海量异构的网络媒体,我们如何实时地总结事件,并发现事件之间的因果关系?的确,我们生活在信息的洪流中,每天都有数十万篇新闻文章发表,社交媒体和互联网论坛上有数百万篇帖子发表,互联网用户产生了数十亿条搜索查询。为了更好地传达用户感兴趣的新闻事件及其大图,构建实时事件推荐系统是必不可少的。我们提出的系统Eventera汇集了来自异构渠道的大量在线媒体,将它们总结为事件,通过桥接事件发现有意义的关联,并生成事件序列图,该序列图提供了现实生活中事件如何随时间相互作用的全景图。我们演示了我们的系统如何帮助用户有效地理解事件及其因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eventera: Real-Time Event Recommendation System from Massive Heterogeneous Online Media
Given massive heterogeneous online media, how can we summarize events, and discover causal relationships among them, in real time? Indeed we are living in a deluge of information, everyday hundreds of thousands of news articles are published, millions of postings from social media and internet forums are written, and billions of search queries are generated by Internet users. To convey user-interested news events and their big pictures for better understanding, building real-time event recommendation system is indispensable. Our proposed system, Eventera, aggregates massive online media from heterogeneous channels, summarizes them into events, discovers meaningful associations by bridging the events, and generates a sequence map of events that provides a big picture of how real life events interact with each other over time. We demonstrate how our system help users understand events and their causal relationships effectively.
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