Score Normalization Using Logistic Regression with Expected Parameters

State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression, using the expected parameters from past queries. Experiments on the Gov2 and CluewebA collection indicate that our method is consistently more precise in predicting the number of relevant documents in the top-n ranks compared to a state-of-the-art generative approach and another parameter estimate for logistic regression.