Mobile application ordering method of situational awareness

Abstract

The invention relates to a mobile application ordering method of situational awareness. The mobile application ordering method comprises the following steps of 1, acquiring a semantization place; 2, acquiring activities; and 3, carrying out application ordering. The invention provides a mobile application ordering method of new situational awareness aiming at the problems that a situation that a user is placed is not considered in the traditional application ordering and the situational awareness capability of existing application ordering is relatively weak. Compared with an existing method, the mobile application ordering method has the advantages that multiple situations are used in a situation awareness process; an advanced situation with higher capabilities is established by a low-level situation; the situational awareness capability is enhanced in the application ordering process, so that the application ordering can more satisfy the individual demands of users.

Description

technical field [0001] The present invention relates to application sorting, in particular to a context-aware mobile application sorting method. Background technique [0002] With the continuous development of the mobile Internet, mobile terminals have become an indispensable part of users' daily life. More and more users are accustomed to completing various daily affairs on mobile terminals. application to facilitate users to complete these transactions. Compared with the traditional Internet method, with the help of rich and colorful applications, users can handle affairs more freely. However, more and more applications will also cause troubles for users, that is, they cannot find the desired application in time among the many applications on the mobile terminal. . [0003] With the rapid growth of the number of applications, it is very necessary to recommend or sort applications. Traditional application recommendation is generally only based on the number of times or f...

AI Technical Summary

Problems solved by technology

However, this recommendation method can only reflect general rules and cannot capture the individual needs of users. At the same time, the increase in application types requires the use of more context-enhanced perception capabilities for smarter recommendations.

Nowadays, the application recommendation based on context-aware technology is weak in context-awareness. Generally, only one context is used, and the expressive ability of the context is weak, which cannot resolve the differences between different users.

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