Title :
Eventera: Real-Time Event Recommendation System from Massive Heterogeneous Online Media
Author :
Dongyeop Kang ; Donggyun Han ; Park, Nahea ; Sangtae Kim ; Kang, U. ; Soobin Lee
Author_Institution :
IT Convergence Lab., Korea Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea
Abstract :
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.
Keywords :
Internet; data mining; query processing; recommender systems; social networking (online); Eventera; Internet forums; causal relationship discovery; causal relationships; event summarization; heterogeneous channels; massive heterogeneous online media; meaningful association discovery; real-time event recommendation system; search queries; sequence map; social media; user-interested news events; Communities; Data models; Detectors; Media; Real-time systems; Search engines; Twitter; event detector; event recommendation; online media; sequence map; summarization;
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
DOI :
10.1109/ICDMW.2014.32
