Method and device for automatically recommending application

Abstract

The invention provides a method and a device for automatically recommending an application. The method comprises the steps of: receiving an application acquisition request submitted from a client by a user, wherein the application acquisition request comprises a user identification; according to the user identification, extracting the existing user behavior information of corresponding user from a user feature library, wherein the user behavior information comprises operation information that a user aims at previously recommended application; according to the user behavior information, determining the type of the application recommended to the user; in an application data set of the type of the application, according to the operation information that the user aims at the previously recommended application, extracting an matched application; and according to the type of the application, generating a corresponding application folder, and placing the matched application in the corresponding application folder for recommending. According to the invention, the individual needs of users can be satisfied, the recommending efficiency is improved and the coverage rate is increased.

Description

technical field [0001] The present application relates to the technical field of information processing, in particular to a method for automatic application recommendation and an apparatus for automatic application recommendation. Background technique [0002] The Internet is an important way for people to obtain information. The main feature of the traditional Internet is that when users look for things they are interested in, they need to conduct a large number of searches through the browser, and at the same time need to manually filter out a large number of irrelevant results. The operation is cumbersome and Takes time and energy. [0003] With the rapid development of Internet technology, people have more and more demands on various network applications. The deployment of applications on the client side is becoming more and more bloated, which not only causes a waste of terminal resources, but also makes it difficult to manage. Even if the client-server architecture i...

AI Technical Summary

Problems solved by technology

[0006] 1. The efficiency is too low, and the recommendation coverage rate for applications is too low. For example, for hundreds of thousands of applications on the platform, manual recommendation can only recommend a few hundred every day

If you want to recommend all apps, it is actually impossible to achieve, and the coverage is too low, because the proportion is too low

[0007]2. This kind of recommendation is completely based on the unified recommendation principle of editors, which is the same for every user and cannot meet the individual needs of users

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