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Mathematical Problems in Engineering
Volume 2015, Article ID 490261, 9 pages
Research Article

Abnormal Profiles Detection Based on Time Series and Target Item Analysis for Recommender Systems

1College of Computer Science, Chongqing University, Chongqing 400030, China
2School of Software Engineering, Chongqing University, Chongqing 400030, China
3Chongqing Health and Family Planning Commission, Chongqing 400000, China

Received 12 December 2014; Accepted 18 May 2015

Academic Editor: Aime’ Lay-Ekuakille

Copyright © 2015 Wei Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Collaborative filtering (CF) recommenders are vulnerable to shilling attacks designed to affect predictions because of financial reasons. Previous work related to robustness of recommender systems has focused on detecting profiles. Most approaches focus on profile classification but ignore the group attributes among shilling attack profiles. Attack profiles are injected in a short period in order to push or nuke a specific target item. In this paper, we propose a method for detecting suspicious ratings by constructing a time series. We reorganize all ratings on each item sorted by time series. Each time series is examined and suspected rating segments are checked. Then we use techniques we have studied in previous study to detect shilling attacks in these anomaly rating segments using statistical metrics and target item analysis. We show in experiments that our proposed method can be effective and less time consuming at detecting items under attacks in big datasets.