Application program recommend method and device

A technology of application programs and recommendation methods, applied in the computer field, which can solve the problems of inability to accurately match user preferences, insufficient novelty of recommendation results, and difficult correlation description.

Inactive Publication Date: 2016-12-07
QINGDAO HISENSE MEDIA NETWORKS CO LTD
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AI Technical Summary

Problems solved by technology

[0004] The first method considers the similarity between user behaviors, and the interpretability of related recommendations is weaker than that based on application content, and it also needs to face the cold start problem.
The second method has insufficient novelty in the recommendation results, and there will be cases where an application is repeatedly recommended multiple times
The third method avoids the problems caused by using a single recommendation method, but for the situation where the similarity of user behavior and the correlation value range of the popularity ranking of application content are quite different, it is difficult to give a simple linear weighting. accurate correlation description, so that recommended applications cannot accurately match user preferences

Method used

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  • Application program recommend method and device
  • Application program recommend method and device
  • Application program recommend method and device

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Embodiment Construction

[0052] In order to make the above objects, features and advantages of the present application more obvious and comprehensible, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0053] One of the core concepts of the embodiments of the present application is to adopt the first ranking value based on the first similarity between the application program and the user behavior information, and the second ranking based on the second similarity between the application program and the preset feature data Value, the calculated ranking score of the application, recommending applications for users according to the ranking score.

[0054] refer to figure 1 , which shows a flow chart of the steps of Embodiment 1 of an application recommendation method of the present application, which may specifically include the following steps:

[0055] Step 101, obtaining user behavior information;

[...

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Abstract

The application embodiment provides an application program recommend method comprising the following steps: obtaining user behavior information; calculating first similarity between a preset application program and the user behavior information, and using the first similarity to form a first ranking value of the preset application program; calculating second similarity between the preset application program and preset characteristic data, and using the second similarity to form a second ranking value of the preset application program; using the first ranking value, the second ranking value and a preset weight coefficient to form a ranking score of the preset application program; using the ranking score to present the application program. The method fuses the weight coefficient with the user behavior information and preset characteristic data, thus forming the ranking score, better combining the relation between the user behavior information and preset characteristic data, and allowing the recommended application program to better comply with user preference.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an application program recommendation method and an application program recommendation device. Background technique [0002] With the rapid increase of applications, various application platforms have developed the recommendation function of "related applications" in order to solve the problem of difficult selection for users. [0003] The relevant recommendation functions of current applications are generally generated based on the following three methods: 1. Calculation of similarity function based on user behavior; 2. Calculation of popularity ranking based on application content; 3. Calculation of popularity ranking based on user behavior similarity and application content Simple weighted average algorithm. [0004] The first method considers the similarity between user behaviors, and the interpretability of related recommendations is weaker than that of populari...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/9535G06F18/22
Inventor 程殿虎于松于芝涛
Owner QINGDAO HISENSE MEDIA NETWORKS CO LTD
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