Abnormal Profiles Detection Based on Time Series and Target Item Analysis for Recommender Systems
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/490261 |
| Summary: | 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. |
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| ISSN: | 1024-123X 1563-5147 |