A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
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Proceedings of the 17th international conference on World Wide Web
Predicting information seeker satisfaction in community question answering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Quality-aware collaborative question answering: methods and evaluation
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
'Helpfulness' in online communities: a measure of message quality
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
Overcoming the J-shaped distribution of product reviews
Communications of the ACM - A View of Parallel Computing
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Evaluating and predicting answer quality in community QA
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Efficient confident search in large review corpora
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Using readability tests to predict helpful product reviews
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Trust, but verify: predicting contribution quality for knowledge base construction and curation
Proceedings of the 7th ACM international conference on Web search and data mining
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Online labor markets such as oDesk and Amazon Mechanical Turk have been growing in importance over the last few years. In these markets, employers post tasks on which remote contractors work and deliver the product of their work. As in most online marketplaces, reputation mechanisms play a very important role in facilitating transactions, since they instill trust and are often predictive of the future satisfaction of the employer. However, labor markets are usually highly heterogeneous in terms of available task categories; in such scenarios, past performance may not be a representative signal of future performance. To account for this heterogeneity, in our work, we build models that predict the performance of a worker based on prior, category-specific feedback. Our models assume that each worker has a category-specific quality, which is latent and not directly observable; what is observable, though, is the set of feedback ratings of the worker and of other contractors with similar work histories. Based on this information, we build a multi-level, hierarchical scheme that deals effectively with the data sparseness, which is inherent in many cases of interest (i.e., contractors with relatively brief work histories). We evaluate our models on a large corpus of real transactional data from oDesk, an online labor market with hundreds of millions of dollars in transaction volume. Our results show an improved accuracy of up to 47% compared to the existing baseline.