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Mobile application classification method for strengthening topic attention mechanism

A technology of mobile applications and classification methods, applied in neural learning methods, computer components, instruments, etc., can solve the problems of long description information, insufficient text representation technology to accurately represent mobile application content texts, feature word order and context language are not considered Environmental information and other issues to achieve the effect of improving accuracy

Active Publication Date: 2020-02-07
HUNAN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

These methods have improved the efficiency and accuracy of mobile application classification to a certain extent, but some problems have not been considered: (1) Usually the description information of mobile applications is long, and the existing text representation techniques (such as LDA topic modeling) are not accurate enough Represents the mobile application content text; (2) Among the many words in the mobile application content description, not every word has the same contribution to the mobile application classification; (3) The word order between the characteristic words in the mobile application content document and the context context information

Method used

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  • Mobile application classification method for strengthening topic attention mechanism
  • Mobile application classification method for strengthening topic attention mechanism
  • Mobile application classification method for strengthening topic attention mechanism

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Experimental program
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Embodiment

[0070] For the mobile application classification method provided by the present invention, the applicant has carried out specific experimental evaluation and analysis, and how the method can improve the accuracy of mobile application classification in this embodiment will be described below in this embodiment.

[0071]Dataset: The public dataset Mobile App Store on the Kaggle website is used as the experimental dataset for mobile application classification. The data set contains 23 categories and a total of 7,197 IOS mobile applications from the App Store. The distribution of the top 20 categories with the largest number is shown in Table 1. For the fairness and accuracy of the experimental results, first remove as much as possible the mobile applications described in non-English in the data set. In addition, the sample distribution of the cleaned data set is uneven. Among them, there are 3,381 mobile applications in the category of 'Games', while there are only 82 mobile appl...

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Abstract

The invention provides a mobile application classification method for strengthening a topic attention mechanism, and the method comprises the steps: firstly carrying out the global topic modeling of amobile application content text through an LSA model, and then carrying out the local hidden representation of the content text through a BiLSTM model; secondly, for a mobile application content representation text rich in global topic information and local semantic information, introducing an attention mechanism to distinguish contribution degrees of different words, and calculating weight values of the contribution degrees; and then, through a full connection layer, finishing classification and prediction of the mobile applications by using a softmax activation function. Experimental results prove that the method provided by the invention can improve the accuracy of mobile application classification indeed, and is more helpful for users to select suitable mobile applications.

Description

technical field [0001] The present invention mainly relates to the related technical field of mobile application classification, in particular to a mobile application classification method that strengthens the theme attention mechanism. Background technique [0002] With the popularization of mobile devices such as smartphones, the number of mobile applications has shown explosive growth. Faced with a huge number of mobile applications with rich content, it is difficult for users to find suitable mobile applications. In order to manage these mobile applications well and facilitate users to download and use them, various mobile application stores have appeared on the Internet, such as domestic Pea Pods, 360 Mobile Assistant, foreign Google Play, App Store, etc. These mobile application stores mainly provide mobile applications for users to download and use in two ways: (1) users search for mobile applications by entering keywords, and the application stores search based on k...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/044G06N3/045G06F18/241
Inventor 曹步清陈俊杰
Owner HUNAN UNIV OF SCI & TECH
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