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Relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly. In this paper, we propose a relevance feedback framework based on Bayesian logistic regression in content-based 3D model retrieval systems to incorporate relevance feedback information. Bayesian logistic regression relevance feedback framework using an active learning algorithm based on variance reduction to actively select documents for user evaluation. Experimental results show that this algorithm achieves higher search accuracy than traditional query refinement schemes.