A Risk and Similarity Aware Application Recommender System
Xiaoyuan Liang
; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
Jie Tian
; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
Xiaoning Ding
; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
Guiling Wang
; Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
APA 6th Edition Liang, X., Tian, J., Ding, X. i Wang, G. (2015). A Risk and Similarity Aware Application Recommender System. Journal of computing and information technology, 23 (4), 303-315. https://doi.org/10.2498/cit.1002537
MLA 8th Edition Liang, Xiaoyuan, et al. "A Risk and Similarity Aware Application Recommender System." Journal of computing and information technology, vol. 23, br. 4, 2015, str. 303-315. https://doi.org/10.2498/cit.1002537. Citirano 27.02.2021.
Chicago 17th Edition Liang, Xiaoyuan, Jie Tian, Xiaoning Ding i Guiling Wang. "A Risk and Similarity Aware Application Recommender System." Journal of computing and information technology 23, br. 4 (2015): 303-315. https://doi.org/10.2498/cit.1002537
Harvard Liang, X., et al. (2015). 'A Risk and Similarity Aware Application Recommender System', Journal of computing and information technology, 23(4), str. 303-315. https://doi.org/10.2498/cit.1002537
Vancouver Liang X, Tian J, Ding X, Wang G. A Risk and Similarity Aware Application Recommender System. Journal of computing and information technology [Internet]. 2015 [pristupljeno 27.02.2021.];23(4):303-315. https://doi.org/10.2498/cit.1002537
IEEE X. Liang, J. Tian, X. Ding i G. Wang, "A Risk and Similarity Aware Application Recommender System", Journal of computing and information technology, vol.23, br. 4, str. 303-315, 2015. [Online]. https://doi.org/10.2498/cit.1002537
Sažetak
As mobile devices, especially smartphones, become more and more popular, the number of mobile applications increases dramatically. Though mobile applications provide users convenience and entertainment, they have potential threat to violate users’ privacy and security. In order to decrease the risk of violation, we propose a risk and similarity aware application recommender system, which recommends high quality applications to users. The system estimates applications’ risk based on the requested permissions and calculates the similarity between applications based on the ratings and the number of ratings. It recommends applications with the lowest risk and highest similarity based on a user’s current applications. The evaluation shows that the system works efficiently in recommending low-risk and high-similarity applications.