Method and device for application recommendation

Abstract

The invention discloses a method and device for application recommendation. The method comprises the steps of obtaining behavior data, relating to applications, of a user; determining attribute data relating to the applications of the user according to the behavior data, relating to the applications, of the user; carrying out matching between labels in detail pages of applications to be recommended and the attribute data relating to the applications of the user, and ranking according to matching degrees; recommending a preset number of applications ranked first to the user. According to the technical scheme, intelligent, personalized and automatic recommendation of the applications is achieved, the defect that the recommendation scheme based on mass behaviors is lacking in personalization is overcome, and user requirements are met.

Description

technical field [0001] The present invention relates to the technical field of the Internet, in particular to a method and device for recommending applications. Background technique [0002] With the development of Web technology, the creation and sharing of Internet information has become easier and easier. A large amount of application data is released to the Internet every day. The extreme explosion of application information makes people more and more search for the applications they need. Difficult, the traditional search technology is a relatively simple tool to help people find application information, and it is also widely used by people, but search engines cannot fully meet users’ needs for application information discovery. The first reason is that it is difficult for users to use appropriate The keywords describe their own application requirements, and the second is that keyword-based information retrieval is not enough in many cases. In such a situation, applica...

AI Technical Summary

Problems solved by technology

[0002] With the development of Web technology, the creation and sharing of Internet information has become easier and easier. A large amount of application data is released to the Internet every day. The extreme explosion of application information makes people more and more search for the applications they need. Difficult, the traditional search technology is a relatively simple tool to help people find application information, and it is also widely used by people, but search engines cannot fully meet users’ needs for application information discovery. The first reason is that it is difficult for users to use appropriate keywords to describe their own application requirements, and second, information retrieval based on keywords is not enough in many cases

In such a situation, an application recommendation system has emerged to recommend applications according to the needs of users. However, most of the current technical solutions for application recommendation systems are recommendations based on public behavior, which are generally static and managed by the system. Personnel settings, or current popular applications calculated based on the feedback statistics of all users of the system, so as to give each user the same recommendation, which cannot meet the personalized needs of users

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