Improving collaborative filtering recommendations by estimating user preferences from clickstream data.

•We propose a method for creating a user-item rating matrix of high quality.•Our method uses item-choice probabilities estimated from clickstream data.•We test two collaborative filtering algorithms: user-based one and NMF-based one.•Our method substantially improves recommender performance of the two algorithms.•We discuss and detect useful features reecting user preferences for items.