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 forum...

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Bibliographic Details
Main Authors: Dongyeop Kang, Donggyun Han, Park, Nahea, Sangtae Kim, Kang, U., Soobin Lee
Format: Conference Proceeding
Language:English
Subjects:
Communities
Data models
Detectors
event detector
event recommendation
Media
online media
Real-time systems
Search engines
sequence map
summarization
Twitter
Online Access:Request full text
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Summary: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.
ISSN:2375-9232
2375-9259
DOI:10.1109/ICDMW.2014.32