IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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The Journal of Machine Learning Research
Convex Optimization
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Uncertainty sampling and transductive experimental design for active dual supervision
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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It is critical for retail enterprises to select good sites or locations to open their stores, especially in current competitive retail market. However, evaluating the goodness of sites in real business applications is a complex problem. That is, how to judge whether the market around a store site is good? We don't know the exact mechanism of how a site can be good and it is hard to have correct site goodness values as supervised labels. The Retail Outlet Site Evaluation (ROSE) tool is designed to learn the site evaluation model by integrating city geographic & demographic data and two kinds of expert knowledge: sample preference and feature preference. The feature preference information can help greatly reduce the required number of sample preferences. It enables our application practicable because it is almost impossible to give such amount of sample preference pairs manually by experts when ranking hundreds of data points. In the experiment and case study part, we show that the ROSE tool can achieve good results and useful for users to do site evaluation work in real cases.