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Mobile game recommendation method for mobile advertising users

A mobile advertising and recommendation method technology, applied in advertising, special data processing applications, instruments, etc., can solve the problems of less game records of mobile phone users and long game cycles of users, so as to overcome the cold start problem, overcome the problem of data sparsity, The effect of improving accuracy and variety

Active Publication Date: 2019-06-18
SOUTH CHINA UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, the field of mobile game recommendation has different characteristics from traditional recommendation: the user's game cycle is long, and mobile phone users have few game records.

Method used

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  • Mobile game recommendation method for mobile advertising users
  • Mobile game recommendation method for mobile advertising users
  • Mobile game recommendation method for mobile advertising users

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with specific examples.

[0043] Such as figure 1 As shown, the mobile phone game recommendation method for mobile advertising users described in this embodiment includes the following steps:

[0044] 1) By segmenting the introduction content of advertisements and games, using graph-based keyword extraction technology to obtain iconic nouns in the introduction content of advertisements and games, that is, entities.

[0045] Entity refers to some iconic nouns, which can be some dynasty names (such as Three Kingdoms, Warring States), personal names (Cao Cao, Song Jiang), place names (Chibi), etc. in the game; in the advertising field, they can be some domain names (such as finance, sports), roles (chic dad, hot mom), etc. For the game introduction data, use common word segmentation tools with part-of-speech tagging, such as HandLP, OpenCLAS, etc., to segment the game introduction data into document-wor...

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Abstract

The invention discloses a mobile phone game recommendation method aiming at mobile advertisement users. When mobile phone users use a mobile phone application, the users often click advertisements released by the mobile phone application. Advertisement clicking behaviors of the mobile phone users can reflect interest preferences of the mobile phone users to a certain extent. Through entity extraction of advertisement introduction files, an advertisement entity set preferred by the mobile phone users can be established according to advertisement clicking records of the mobile phone users. Aiming at users with game records among the mobile phone advertisement users, a game entity set and a game set preferred by the users can be further extracted, and the two sets together with the advertisement entity set can jointly reflect the users' interest preferences. Aiming at the mobile advertisement users with the game records, a neighbor user set is established through cosine similarity computation of a user model, and personalized recommendation can be carried out; and aiming at the mobile advertisement users without the game records, a cosine similarity between the users and the advertisement users with the game records is computed, and a neighbor user set is established, so that personalized recommendation can be carried out.

Description

technical field [0001] The invention relates to the technical field of mobile phone game recommendation, in particular to a mobile phone game recommendation method for mobile advertising users. Background technique [0002] With the rapid development of the Internet, people face more and more information in their daily life. In order to solve people's confusion in the face of massive information, the recommendation system came into being. There are currently three types of recommendation algorithms: collaborative filtering-based recommendation systems, content-based recommendation systems, and hybrid recommendation systems. The recommendation system based on collaborative filtering mainly includes technologies such as User-Based, Item-Based and Model-Based. The Amazon shopping website mainly uses Item-Based technology to make personalized recommendations to users by analyzing the user's historical records. The content-based recommendation system mainly constructs feature ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06Q30/02
CPCG06F16/9535G06Q30/0271
Inventor 董守斌魏向前胡金龙袁华董守玲
Owner SOUTH CHINA UNIV OF TECH
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