Vote Calibration in Community Question-Answering Systems

被引:0
作者
Chen, Bee-Chung [1 ]
Dasgupta, Anirban [2 ]
Wang, Xuanhui [3 ]
Yang, Jie [4 ]
机构
[1] LinkedIn, Mountain View, CA 94043 USA
[2] Yahoo Labs, Sunnyvale, CA USA
[3] Facebook, Menlo Pk, CA USA
[4] Google, Mountain View, CA USA
来源
SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2012年
关键词
Reputation; user modeling; crowdsourcing; community question-answering;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
User votes are important signals in community question-answering (CQA) systems. Many features of typical CQA systems, e.g. the best answer to a question, status of a user, are dependent on ratings or votes cast by the community. In a popular CQA site, Yahoo! Answers, users vote for the best answers to their questions and can also thumb up or down each individual answer. Prior work has shown that these votes provide useful predictors for content quality and user expertise, where each vote is usually assumed to carry the same weight as others. In this paper, we analyze a set of possible factors that indicate bias in user voting behavior - these factors encompass different gaming behavior, as well as other eccentricities, e.g., votes to show appreciation of answerers. These observations suggest that votes need to be calibrated before being used to identify good answers or experts. To address this problem, we propose a general machine learning framework to calibrate such votes. Through extensive experiments based on an editorially judged CQA dataset, we show that our supervised learning method of content-agnostic vote calibration can significantly improve the performance of answer ranking and expert ranking.
引用
收藏
页码:781 / 790
页数:10
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