Information-based objective functions for active data selection
Neural Computation
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to Optimally Schedule Internet Banner Advertisements
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Sales promotions on the internet
WOEC'98 Proceedings of the 3rd conference on USENIX Workshop on Electronic Commerce - Volume 3
Learning from labeled and unlabeled data using a minimal number of queries
IEEE Transactions on Neural Networks
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Marketing decisions are typically made on the basis of research conducted using direct mailings, mall intercepts, telephone interviews, focused group discussion, and the like. These methods of marketing research can be time-consuming and expensive, and can require a large amount of effort to ensure accurate results. This paper presents a novel approach for conducting online marketing research based on several concepts such as active learning, matched control and experimental groups, and implicit and explicit experiments. These concepts, along with the opportunity provided by the increasing numbers of online shoppers, enable rapid, systematic, and cost-effective marketing research.