DOI: 10.3724/SP.J.1249.2019.01043

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2019/36:1 PP.43-51

The application of optimization algorithm based on incremental learning in app usage prediction

With the increasing number of apps on smartphones, it becomes more and more difficult to query the target app accurately. It is increasingly important and necessary to predict the next app to be launched quickly and accurately. There are two kinds of problems in using historical user data to predict the next app algorithm:One is that some algorithms do not consider the increment of training data over time, which leads to the decrease of the prediction accuracy over time. The other is that although some algorithms take the incremental data into account, they increase the time required to rebuild the model, thus greatly increase the overall time-consuming. To reduce the remodeling time, we utilize an incremental k-nearest neighbors (IkNN) model algorithm to implement a Predictor prediction system. When the IkNN model is used for predicting the next app usage, a new problem is found. When modeling with training data, the classification accuracy reduces with the increase of number of features of an app. After studying the relationship among the context features of an app, we design a cluster effective value (CEV) which can compensate the errors induced by multidimensional features and thus improve the prediction accuracy. It is shown that the IkNN model algorithm with CEV has a higher and more stable prediction accuracy than that of the algorithm without CEV. The large-scale experiments show that the Predictor can reduce the remodeling time and improve the prediction accuracy.

Key words:pattern recognition,app usage prediction,clustering,incremental learning,big data

ReleaseDate:2019-01-28 09:56:34

[1] BAEZA-YATES R, JIANG D, SILVESTRI F, et al. Predicting the next app that you are going to use[C]//Proceedings of the 8th International Conference on Web Search and Data Mining. New York, USA:ACM, 2015:285-294.

[2] MITRA P, MURTHY C A, PAL S K. Density-based multiscale data condensation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(6):734-747.

[3] MENG Weizhi, LI Wenjuan, KWOK L F. Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection[J]. Security and Communication Networks:Security Communication Networks. 2015:8(18):3883-3895.

[4] PANDYA D H, UPADHYAY S H, HARSHA S P. Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN[J]. Expert Systems with Applications:an International Journal, 2013, 40(10):4137-4145.

[5] ZOU Xun, ZHANG Wangsheng, LI Shijian, et al. Prophet:what app you wish to use next[C]//Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. New York, USA:ACM, 2013:167-170.

[6] PARATE A, BÖHMER M, CHU D, et al. Practical prediction and prefetch for faster access to applications on mobile phones[C]//Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, USA:ACM, 2013:275-284.

[7] YAN Tingxin, CHU D, GANESAN D, et al. Fast app launching for mobile devices using predictive user context[C]//Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. New York, USA:ACM, 2012:113-126.

[8] HUANG Ke, ZHANG Chunhui, MA Xiaoxiao, et al. Predicting mobile application usage using contextual information[C]//Proceedings of the ACM Conference on Ubiquitous Computing. New York, USA:ACM, 2012:1059-1065.

[9] XIANG Zhengzhe, DENG Shuiguang, LIU Songguo, et al. CAMER:a context-aware mobile service recommendation system[C]//IEEE International Conference on Web Services. San Francisco, USA:IEEE, 2016:292-299.

[10] CAO Hong, LIN Miao. Mining smartphone data for app usage prediction and recommendations:a survey[J]. Pervasive and Mobile Computing, 2017, 37:1-22.

[11] PAN Wei, AHARONY N, PENTLAND A. Composite social network for predicting mobile apps installation[EB/OL].[2011-01-22].

[12] KESHET J, KARIV A, DAGAN A, et al. Context-based prediction of app usage[J]. Computer Science, 2015.

[13] ZHANG Chunhui, DING Xiang, CHEN Guangling, et al. Nihao:a predictive smartphone application launcher[C]//International Conference on Mobile Computing, Applications, and Services. Osaka, Japan:Springer, 2013:294-313.

[14] MILOUD-AOUIDATE A, BABA-ALI A R. Survey of nearest neighbor condensing techniques[J]. International Journal of Advanced Computer Science and Application, 2011, 2(11):59-64.

[15] BAILEY T, JAIN A K. A note on distance-weighted k-nearest neighbor rules[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1978, 8(4):311-313.

[16] LIANG Tianming, XU Xinzheng, XIAO Pengcheng. A new image classification method based on modified condensed nearest neighbor and convolutional neural networks[J]. Pattern Recognition Letters, 2017, 94:105-111.

[17] KUMAR R R, VISWANATH P, BINDU C S. Nearest neighbor classifiers:a review[J]. International Journal of Computational Intelligence Research, 2017, 13(2):303-311.

[18] CHAUDHURI D, MURTHY C A, CHAUDHURI B B. A modified metric to compute distance[J]. Pattern Recognition, 1992, 25(7):667-677.

[19] CELANOVIC N, BOROYEVICH D. A fast space-vector modulation algorithm for multilevel three-phase converters[J]. IEEE transactions on industry applications, 2001, 37(2):637-641.

[20] ZHAO Geng, XUAN Kefeng, TANIAR D, et al. Incremental k-nearest-neighbor search on road networks[J]. Journal of Interconnection Networks, 2008, 9(4):455-470.