doi:

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


Abstract:
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



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