A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems
Recommender systems are widely used, in social networks and online stores, to overcome the problems caused by the large amount of information. Most of these systems use a collaborative filtering method to generate recommendations to the users. But, as in this method users’ feedback is considered for...
| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
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University of Tehran
2017-12-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_62727_00f733abc13f9d297f4ef0696ccbda2d.pdf |
| _version_ | 1811248498026217472 |
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| author | javad nehriri sasan hosseinali zadeh |
| author_facet | javad nehriri sasan hosseinali zadeh |
| author_sort | javad nehriri |
| collection | DOAJ |
| description | Recommender systems are widely used, in social networks and online stores, to overcome the problems caused by the large amount of information. Most of these systems use a collaborative filtering method to generate recommendations to the users. But, as in this method users’ feedback is considered for recommendations, it can be significantly erroneous by the malicious people. In other words, there may be some users who open fake profiles and vote one-sided or biased in the system that may cause disturbance in providing proper recommendations to other users. This kind of damage is said to be shiling attacks. If the attackers succeed, the user's trust in the recommender systems will reduce. In recent years, efficient attack detection algorithms have been proposed, but each has its own limitations. In this paper, we use profile-based and item-based algorithms to provide a new mechanism to significantly reduce the detection error for shilling attacks. |
| first_indexed | 2024-04-12T15:29:11Z |
| format | Article |
| id | doaj.art-6ba773949e5547a3ab6579174c00a83f |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-04-12T15:29:11Z |
| publishDate | 2017-12-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-6ba773949e5547a3ab6579174c00a83f2022-12-22T03:27:10ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592017-12-019487189210.22059/jitm.2017.237094.208862727A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systemsjavad nehriri0sasan hosseinali zadeh1MSc. Student, Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranAssistant Prof., Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranRecommender systems are widely used, in social networks and online stores, to overcome the problems caused by the large amount of information. Most of these systems use a collaborative filtering method to generate recommendations to the users. But, as in this method users’ feedback is considered for recommendations, it can be significantly erroneous by the malicious people. In other words, there may be some users who open fake profiles and vote one-sided or biased in the system that may cause disturbance in providing proper recommendations to other users. This kind of damage is said to be shiling attacks. If the attackers succeed, the user's trust in the recommender systems will reduce. In recent years, efficient attack detection algorithms have been proposed, but each has its own limitations. In this paper, we use profile-based and item-based algorithms to provide a new mechanism to significantly reduce the detection error for shilling attacks.https://jitm.ut.ac.ir/article_62727_00f733abc13f9d297f4ef0696ccbda2d.pdfCollaborative filteringHHT algorithmRecommender systemsSDF algorithmShilling attacks |
| spellingShingle | javad nehriri sasan hosseinali zadeh A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems Journal of Information Technology Management Collaborative filtering HHT algorithm Recommender systems SDF algorithm Shilling attacks |
| title | A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems |
| title_full | A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems |
| title_fullStr | A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems |
| title_full_unstemmed | A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems |
| title_short | A New Mechanism to Improve the Detection Rate of Shilling Attacks in the Recommender Systems |
| title_sort | new mechanism to improve the detection rate of shilling attacks in the recommender systems |
| topic | Collaborative filtering HHT algorithm Recommender systems SDF algorithm Shilling attacks |
| url | https://jitm.ut.ac.ir/article_62727_00f733abc13f9d297f4ef0696ccbda2d.pdf |
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