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Collaborative filtering systems are vulnerable to shilling attacks in which malicious users bias the systems' recommendation output by inserting fake profiles. While many approaches have been proposed to detect shilling attacks, they suffer from low precision. To solve this problem, an ensemble method for detecting shilling attacks based on ordered item sequences is proposed. Firstly, by analyzing the differences of rating patterns between genuine and attack profiles, we construct ordered popular item sequences and ordered novelty item sequences, and based on which, the popular and novelty item rating series are constructed for each user profile. Secondly, we propose six features to characterize the attack profiles. Particularly, we extract two features based on the popular and novelty item rating series. We partition the item set according to the ordered item sequences and combine them with mutual information to extract another four features. Finally, we propose an ensemble framework to detect shilling attacks. In particular, we create base training sets with great diversities using bootstrap resampling technique. Based on these base training sets, we train decision tree algorithm to generate diverse base classifiers. The simple majority voting strategy is used to combine the predictive results of these base classifiers. Experimental results indicate that ensemble method for detecting shilling attacks based on ordered item sequences can significantly improve the precision while maintaining a high recall. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]