Skip to main content
SpringerLink
Log in

Personalized app recommendation based on app permissions

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

With the development of science and technology, the popularity of smart phones has made exponential growth in mobile phone application market. How to help users to select applications they prefer has become a hot topic in recommendation algorithm. As traditional recommendation algorithms are based on popularity and download, they inadvertently fail to recommend the desirable applications. At the same time, many users tend to pay more attention to permissions of those applications, because of some privacy and security reasons. There are few recommendation algorithms which take account of apps’ permissions, functionalities and users’ interests altogether. Some of them only consider permissions while neglecting the users’ interests, others just perform linear combination of apps’ permissions, functionalities and users’ interests to implement top-N recommendation. In this paper, we devise a recommendation method based on both permissions and functionalities. After demonstrating the correlation of apps’ permissions and users’ interests, we design an app risk score calculating method ARSM based on app-permission bipartite graph model. Furthermore, we propose a novel matrix factorization algorithm MFPF based on users’ interests, apps’ permissions and functionalities to handle personalized app recommendation. We compare our work with some of the state-of-the-art recommendation algorithms, and the results indicate that our work can improve the recommendation accuracy remarkably.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

References

  1. Baeza-Yates, R., Jiang, D., Silvestri, F., Harrison, B.: Predicting the next app that you are going to use Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp 285–294. ACM (2015)

  2. Chia, P.H., Yamamoto, Y., Asokan, N.: Is this app safe?: A large scale study on application permissions and risk signals Proceedings of the 21st International Conference on World Wide Web, pp 311–320. ACM (2012)

  3. Guo, G., Zhang, J., Yorke-Smith, N.: Trustsvd: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings AAAI, pp 123–129 (2015)

  4. Huang, J., Peng, M., Wang, H., Cao, J., Gao, W., Zhang, X.: A probabilistic method for emerging topic tracking in microblog stream. World Wide Web, pp. 1–26 (2016)

  5. Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434. ACM (2008)

  6. Koren, Y., Bell, R., Volinsky, C., et al.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  7. Li, M., Sun, X., Wang, H., Zhang, Y., Zhang, J.: Privacy-aware access control with trust management in Web service. World Wide Web 14(4), 407–430 (2011)

    Article  Google Scholar 

  8. Lin, J., Sugiyama, K., Kan, M.Y., Chua, T.S.: Addressing cold-start in app recommendation: Latent user models constructed from twitter followers Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 283–292. ACM (2013)

  9. Liu, B., Kong, D., Cen, L., Gong, N.Z., Jin, H., Xiong, H.: Personalized mobile app recommendation: Reconciling app functionality and user privacy preference Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp 315–324. ACM (2015)

  10. Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: Social recommendation using probabilistic matrix factorization Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp 931–940. ACM (2008)

  11. Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. 16 (1), 1–20 (2016)

    Article  Google Scholar 

  12. Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization Proceedings of the 5th ACM Conference on Digital Libraries, pp 195–204. ACM (2000)

  13. Peng, H., Gates, C., Sarma, B., Li, N., Qi, Y., Potharaju, R., Nita-Rotaru, C., Molloy, I.: Using probabilistic generative models for ranking risks of android apps Proceedings of the 2012 ACM Conference on Computer and Communications Security, pp 241–252. ACM (2012)

  14. Peng, M., Gao, B., Zhu, J., Huang, J., Yuan, M., Li, F.: High quality information extraction and query-oriented summarization for automatic query-reply in social network. Expert Syst. Appl. 44, 92–101 (2016)

    Article  Google Scholar 

  15. Peng, M., Huang, J.J., Ghani, N., Sun, S.T., Wu, B., He, Y.X., Wen, W.D.: Micro-blogger influence analysis based on user features. J. Internet Technol. 14 (2), 307–314 (2013)

    Google Scholar 

  16. Saad, D.: Online algorithms and stochastic approximations. Online Learning

  17. Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo Proceedings of the 25th International Conference on Machine Learning, pp 880–887. ACM (2008)

  18. Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization NIPS, vol. 20, pp 1–8 (2011)

  19. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms Proceedings of the 10th International Conference on World Wide Web, pp 285–295. ACM (2001)

  20. Shi, K., Ali, K.: Getjar mobile application recommendations with very sparse datasets Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 204–212. ACM (2012)

  21. Tan, C., Liu, Q., Chen, E., Xiong, H.: Prediction for mobile application usage patterns Nokia MDC Workshop, vol. 12 (2012)

  22. Wang, H., Cao, J., Zhang, Y.: A flexible payment scheme and its role-based access control. IEEE Trans. Knowl. Data Eng. 17(3), 425–436 (2005)

    Article  Google Scholar 

  23. Yu, K., Zhang, B., Zhu, H., Cao, H., Tian, J.: Towards personalized context-aware recommendation by mining context logs through topic models Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 431–443. Springer (2012)

  24. Zhang, J., Tao, X., Wang, H.: Outlier detection from large distributed databases. World Wide Web 17(4), 539–568 (2014)

    Article  Google Scholar 

  25. Zhang, Y., Shen, Y., Wang, H., Yong, J.: On secure wireless communications for iot under eavesdropper collusion. IEEE Trans. Autom. Sci. Eng. 13(3), 1281–1293 (2016)

    Article  Google Scholar 

  26. Zhang, Y., Shen, Y., Wang, H., Zhang, Y., Jiang, X.: On secure wireless communications for service oriented computing. IEEE Transactions on Services Computing (2015)

  27. Zhu, H., Chen, E., Yu, K., Cao, H., Xiong, H., Tian, J.: Mining personal context-aware preferences for mobile users 2012 IEEE 12th International Conference on Data Mining, pp 1212–1217. IEEE (2012)

  28. Zhu, H., Xiong, H., Ge, Y., Chen, E.: Mobile app recommendations with security and privacy awareness Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 951–960. ACM (2014)

Download references

Acknowledgments

This work is supported by the National Science Foundation of China (NSFC, No.61472291 and No.41472288).

Author information

Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan, China

    Min Peng, Guanyin Zeng, Zhaoyu Sun & Gang Tian

  2. School of Technology, Nanjing Audit University, Nanjing, 211815, People’s Republic of China

    Jiajia Huang

  3. Centre for Applied Informatics, Victoria University, Melbourne, Australia

    Hua Wang

Authors
  1. Min Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guanyin Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaoyu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiajia Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Tian.

Additional information

This article belongs to the Topical Collection: Special Issue on Security and Privacy of IoT

Guest Editors: Tarik Taleb, Zonghua Zhang, and Hua Wang

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, M., Zeng, G., Sun, Z. et al. Personalized app recommendation based on app permissions. World Wide Web 21, 89–104 (2018). https://doi.org/10.1007/s11280-017-0456-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-017-0456-y

Keywords

Search

Navigation