A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Predictors of answer quality in online Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Quality-aware collaborative question answering: methods and evaluation
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Ranking community answers by modeling question-answer relationships via analogical reasoning
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Incorporating Participant Reputation in Community-Driven Question Answering Systems
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Expert identification in community question answering: exploring question selection bias
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Using graded-relevance metrics for evaluating community QA answer selection
Proceedings of the fourth ACM international conference on Web search and data mining
What Makes a High-Quality User-Generated Answer?
IEEE Internet Computing
Competition-based user expertise score estimation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Competition-based networks for expert finding
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Estimating sharer reputation via social data calibration
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.