Mobile application use behavior prediction method based on Entity Embedding and TCN model

Abstract

The invention discloses a mobile application use behavior prediction method based on Entity Embedding and a TCN model. The method comprises: obtaining a mobile application use information original data set of a user; preprocessing the original data set of the mobile application use information; performing Entity Embedding on the classified data on the basis of an Embedding layer of a neural network, and constructing feature data; constructing a TCN network prediction model by taking the feature data as input; and obtaining an optimal TCN network prediction model through training and verification, and predicting a to-be-used mobile application. The influence of an App use sequence and a context environment on App use is comprehensively considered, an Entity Embedding feature extraction method and a TCN neural network are applied to mobile application use behavior prediction, the tedious feature processing process of a traditional machine learning model is avoided, feature data are extracted by using an Entity Embedding method, and the prediction capability of the TCN model can be improved by customizing the dimension of the feature data input into the TCN model.

Description

technical field [0001] The invention relates to mobile application use behavior prediction technology, in particular to a mobile application use behavior prediction method based on Entity Embedding and TCN models. Background technique [0002] Today, with highly developed Internet applications and mobile terminals, various mobile applications (Apps) have brought great convenience to our travel, shopping, entertainment, etc. As users install more and more multifunctional and complex applications on mobile terminals, it not only increases the time and difficulty for users to find mobile applications, but also background applications that are not closed in time will also occupy certain resources of smartphones, which may cause mobile phones to run Unsmooth, which greatly affects the user experience and satisfaction. [0003] If data mining technology can be used to predict the behavior of users using apps, that is, to accurately predict the apps that users will use in the next...

AI Technical Summary

Problems solved by technology

Although the use of deep learning can avoid cumbersome data feature processing and construction, it is time-consuming to collect enough training sets for each user, and model updates require high training costs

Images