The nature of statistical learning theory
The nature of statistical learning theory
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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We present a novel content-based re-ranking scheme for enhancing the precision of video retrieval on the web. We use ontology specified knowledge of the video domain to map user queries to domain-based concepts. The user preferences are learned implicitly from the web logs of users' interaction with a video search engine. A ranking SVM is trained for each concept to learn the ranking function which incorporates user preferences for the concept. The videos are represented by a set of ingeniously derived content based features which are based on MPEG-7 descriptors. Our re-ranking scheme thus effectively re-ranks results for new text queries submitted to our video retrieval system, leading to better satisfaction of the users' information need.