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Learning ontology for personalized video retrieval
H. Ghosh, P. Poornachander, A. Mallik,
Published in
Pages: 39 - 46
This paper proposes a new method for using implicit user feedback from clickthrough data to provide personalized ranking of results in a video retrieval system. The annotation based search is complemented with a feature based ranking in our approach. The ranking algorithm uses belief revision in a Bayesian Network, which is derived from a multimedia ontology that captures the probabilistic association of a concept with expected video features. We have developed a content model for videos using discrete feature states to enable Bayesian reasoning and to alleviate on-line feature processing overheads. We propose a reinforcement learning algorithm for the parameters of the Bayesian Network with the implicit feedback obtained from the clickthrough data. Copyright 2007 ACM.
About the journal
JournalProceedings of the ACM International Multimedia Conference and Exhibition