Mobile application pushing method and system

Abstract

The invention provides a mobile application pushing method. The mobile application pushing method includes determining more than one mobile application category with the highest relevancy to mobile applications designed by a user according to relevancy of pre-generated mobile application categories; calculating recommendation levels of the mobile applications under the mobile application categories according to pre-generated weighted values of the mobile applications; extracting the mobile application with higher rankings of the recommendation levels under each mobile application category; and selecting more than one mobile application which has the highest recommendation level as a recommendation result according to the number of pre-set recommendation results, and pushing the more than one mobile application to users. The invention further provides a mobile application pushing system. According to the mobile application pushing method and the system, diversity of the recommended mobile applications can be effectively improved.

Description

【Technical field】 [0001] The invention relates to the field of Internet applications, in particular to a method and system for pushing mobile applications. 【Background technique】 [0002] At present, mobile application stores will push some mobile applications to users when they download or browse applications, in order to recommend mobile applications to users. Recommendation algorithms such as neighbors and collaborative filtering are used to generate recommendation results. Therefore, the prior art recommends mobile applications based on the correlation between mobile applications. [0003] Therefore, the current recommendation method for mobile applications has the following problems: [0004] 1. Since the recommendation basis is based on the correlation between mobile applications, the content of the recommended mobile applications is too similar, and it is impossible to recommend a variety of mobile applications to users, thereby failing to stimulate users' demand for...

AI Technical Summary

Problems solved by technology

[0004] 1. Since the recommendation basis is based on the correlation between mobile applications, the content of the recommended mobile applications is too similar, and it is impossible to recommend a variety of mobile applications to users, thus failing to stimulate users' demand for mobile applications

[0005] 2. Since the newly-added mobile application does not have user history logs, it is impossible to count the correlation between the newly-added mobile application and other mobile applications, so it is impossible to recommend the newly-added mobile application to the user when viewing or downloading the mobile application. Users, can't solve the cold start problem of the newly added mobile application

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