Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Randomized algorithms
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Text mining for product attribute extraction
ACM SIGKDD Explorations Newsletter
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A Data Mining Formalization to Improve Hypergraph Minimal Transversal Computation
Fundamenta Informaticae
A new rank correlation coefficient for information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Feature subsumption for opinion analysis
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
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
Selecting a comprehensive set of reviews
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Estimating entity importance via counting set covers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Item reviews are a valuable source of information for potential buyers, who are looking for information on a product's attributes before making a purchase decision. This search of information is often hindered by overwhelming numbers of available reviews, as well as low-quality and noisy content. While a significant amount of research has been devoted to filtering and organizing review corpora toward the benefit of the buyers, a crucial part of the reviewing process has been overlooked: reviewer satisfaction. As in every content-based system, the content-generators, in this case the reviewers, serve as the driving force. Therefore, keeping the reviewers satisfied and motivated to continue submitting high-quality content is essential. In this paper, we propose a system that helps potential buyers by focusing on high-quality and informative reviews, while keeping reviewers content and motivated.