| Autor: |
Yudhvir Singh, M Ashish Kumar, Harkesh Sehrawat, Vikas Siwach |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
INFORMATION TECHNOLOGY IN INDUSTRY. 9:472-478 |
| ISSN: |
2203-1731 |
| DOI: |
10.17762/itii.v9i1.155 |
| Popis: |
Recommender systems are the backbone of all the prediction-based service platforms e.g. Facebook, Amazon, LinkedIn etc. Even companies now a days are using the recommender systems to show users personalized ads. These service providers capture the right audience for their services/ products and hence, improve overall sales. Social networking platforms are using recommender systems for connecting people of similar interests which is almost impossible without recommender systems. Collaborative filtering-based recommender system is most widely used recommender system. It is used in this research to predict the rating for a specific movie. Accuracy of the prediction define the performance of the overall system. The quality of predictions is degraded by the attackers by injection of fake profiles. In this paper, the various types of profile injection attacks are explained and the attack scenario gets extended to measure the performance of these attacks. Empirical results on the real world publicly available data set shows that these attacks are highly vulnerable. The impact of these attacks in several conditions has been measured and it is tried to find the scenarios where these attacks are more powerful. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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