Abstract
A large amount and different types of mobile applications are being offered to end users via app markets. Existing mobile app markets generally recommend the most popular mobile apps to mobile users for purpose of facilitate the proper selection of mobile apps. However, these apps normally generate network traffic, which will consumes user mobile data plan and may even cause potential security issues. Therefore, more and more mobile users are hesitant or even reluctant to use the mobile apps that are recommended by the mobile app markets. To fill this crucial gap, we propose a mobile app recommendation approach which can provide app recommendations by considering both the app popularity and their traffic cost. To achieve this goal, we first estimate app network traffic score based on bipartite graph. Then, we propose a flexible approach based on Benefit-Cost analysis, which can recommend apps by maintaining a balance between the app popularity and the traffic cost concern. Finally, we evaluate our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness and efficiency of our approach.
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References
Grace, M.C., Zhou, W., Jiang, X., Sadeghi, A.: Unsafe exposure analysis of mobile in-app advertisements. In: Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 101–112 (2012)
Dai, S., Tongaonkar, A., Wang, X., Nucci, A., Song, D.: NetworkProfiler: towards automatic fingerprinting of android apps. In: 2013 Proceedings IEEE INFOCOM, pp. 809–817 (2013)
Falaki, H., Lymberopoulos, D., Mahajan, R., Kandula, S., Estrin, D.: A first look at traffic on smartphones. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 281–287 (2013)
Yan, B., Chen, G.L.: AppJoy: personalized mobile application discovery. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 113–126 (2011)
Fu, Z.J., Sun, X.M., Liu, Q., Zhou, L., Shu, J.G.: Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans. Commun. 98(1), 190–200 (2015)
Xie, H.R., Li, Q., Mao, X.D., Li, X.D., Cai, Y., Rao, Y.H.: Community-aware user profile enrichment in folksonomy. Neural Netw. 58, 111–121 (2014)
Sentz, K., Ferson, S.: Combination of evidence in Dempster-Shafer theory. Technical report, Sandia National Laboratories (2014)
Cost-benefit analysis. http://en.wikipedia.org/wiki/Cost-benefit_analysis
Zhang, W.N., Wang, J., Chen, B.W., Zhao, X.X.: To personalize or not: a risk management perspective. In: Proceedings of the 7th ACM Conference on Recommender Systems (2013)
Luo, C.Y., Xiong, H., Zhou, W.J., Guo, Y.H., Deng, G.S.: Enhancing investment decisions in P2P lending: an investor composition perspective. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 292–300 (2011)
Petsas, T., Papadogiannakis, A., Polychronakis, M., Markatos, E.P., Karagiannis, T.: Rise of the planet of the apps: a systematic study of the mobile app ecosystem. In: Proceedings of the 2013 Conference on Internet Measurement Conference, pp. 277–290 (2013)
Chris, B., Tal, S., Erin, R., Ari, L., Matt, D., Nicole, H., Greg, H.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 89–96 (2005)
Yoav, F., Raj, I., Robert, E.S., Yoram, S.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4, 933–969 (2005)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th International Conference on Machine Learning, pp. 129–136 (2007)
Kong, Y., Zhang, M.J., Ye, D.Y.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl. Based Syst. 115, 123–132 (2017)
Bae, D., Han, K.J., Park, J., Yi, M.Y.: AppTrends: a graph-based mobile app recommendation system using usage history. In: International Conference on Big Data and Smart Computing, pp. 210–216 (2015)
Xu, X.Y., Dutta, K., Datta, A.: Functionality-based mobile app recommendation by identifying aspects from user reviews. In: Proceedings of the International Conference on Information Systems - Building a Better World through Information Systems, pp. 1–10 (2014)
Xie, H.R., Li, X.D., Wang, T., Chen, L., Li, K., Wang, F.L., Cai, Y., Li, Q., Min, H.Q.: Personalized search for social media via dominating verbal context. Neurocomputing 172, 27–37 (2016)
Liu, Q., Cai, W.D., Shen, J., Fu, Z.J., Liu, X.D., Linge, N.: A speculative approach to spatialtemporal efficiency with multiobjective optimization in a heterogeneous cloud environment. Secur. Commun. Netw. 9(17), 4002–4012 (2016)
Acknowledgements
This work is supported by the Science and Technology Projects of Hunan Province (No.2016JC2074), the Research Foundation of Education Bureau of Hunan Province, China(No.16B085), the Open Research Fund of Key Laboratory of Network Crime Investigation of Hunan Provincial Colleges (No.2016WLFZZC008), the National Science Foundation of China(No.61471169), the Key Lab of Information Network Security, Ministry of Public Security (No.C16614).
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Su, X., Liu, X., Lin, J., Tong, Y. (2017). An Android App Recommendation Approach by Merging Network Traffic Cost. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10602. Springer, Cham. https://doi.org/10.1007/978-3-319-68505-2_28
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DOI: https://doi.org/10.1007/978-3-319-68505-2_28
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