Application recommendation method and application recommendation system

Abstract

The invention provides an application recommendation method and an application recommendation system. The application recommendation method includes the following steps: access behavior data of multiple reference users to preset multiple applications are obtained, the multiple reference users are subjected to grouping according to the access behavior data, and each of the multiple applications has a corresponding application class; a group which a target user belongs to is determined according to access behavior data of the target user to the multiple applications, access weighted values of the target user to different application classes are subjected to statistics, and the application classes with the access weighted values which are in a preset range are regarded as interest application classes; and the applications which belong to the interest application classes are extracted from the multiple applications which are accessed by all the reference users and are in the group which the target user belongs to, and then the applications are recommended to the target user. The application recommendation method and the application recommendation system can occupy fewer system resources when the applications are recommended.

Description

technical field [0001] The present application relates to the field of network technologies, and in particular to an application recommendation method and an application recommendation device. Background technique [0002] With the continuous increase of people's demand for network applications, personalized recommendation services have gradually entered people's lives. Personalized recommendation services refer to targeted recommendations for different visitors. At present, many emerging websites use personalized recommendation services to attract consumers to browse. Specifically, through the analysis of each visitor's visit behavior, personalized recommendations are given to visitors according to the analysis results. [0003] At present, the more commonly used recommendation methods mainly include the following two types: [0004] 1. Content-Based Filtering (CB, Content-Based Filtering) [0005] This method is based on the access data of the target user (that is, the o...

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Problems solved by technology

[0009] When using the content filtering method, it is difficult to find videos that can be recommended due to the limited information of some videos; the collaborative filtering method does not need to judge based on the relevant information of the video, and can solve these problems of the content filtering method. The collaborative filtering method needs to filter out other users or videos with high similarity to predict the scores of candidate videos. These operations take up a lot of system resources, and some videos may not have the scores of relevant users, so it is impossible to further predict the score of the candidate video. The rating of the videos prevents these videos from being recommended

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