DMCA
Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement (2009)
Cached
Download Links
| Venue: | WWW 2009 MADRID! TRACK: DATA MINING / SESSION: GRAPH ALGORITHMS |
| Citations: | 50 - 2 self |
BibTeX
@MISC{Bian09learningto,
author = {Jiang Bian and Eugene Agichtein and Yandong Liu and Hongyuan Zha and Ding Zhou},
title = { Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement},
year = {2009}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely- increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data – which
Keyphrases
recognize reliable user coupled mutual reinforcement social medium general purpose web search past question cqa site answer varies viable alternative yahoo answer valuable knowledge repository automatic question information retrieval previous approach popular forum large fraction community question answering last year cqa portal high quality content gold mine large amount
