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A personalized recommendation method based on users and articles

A technology for recommending methods and items, applied in marketing, buying and selling/lease transactions, etc., can solve the problems of over-focusing on high-popularity items, excessive diffusion, and reducing the popularity of popular items, so as to improve diversity, suppress excessive diffusion, and improve The effect of improved performance

Active Publication Date: 2019-06-21
XIDIAN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In physical dynamics, material diffusion based on a bipartite network is widely used in recommendation systems. However, material diffusion has the disadvantages of over-diffusion and too much attention to high-popularity items. Most of the previous studies focused on reducing popularity. item popularity

Method used

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  • A personalized recommendation method based on users and articles
  • A personalized recommendation method based on users and articles
  • A personalized recommendation method based on users and articles

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Embodiment Construction

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0056] figure 1 A flowchart of a method for personalized recommendation based on users and items is provided for an embodiment of the present invention. Such as figure 1 As shown, the method includes:

[0057] Step S1, obtaining item datasets and user datasets with different popularity.

[0058] Step S2, establishing a hyperparameter equilibrium diffusion model.

[0059] Step S3, according to the item data set, the user data set and the hyperparameter bala...

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Abstract

The invention discloses a personalized recommendation method based on users and articles. The method comprises the following steps: obtaining an article data set and a user data set with different popularity degrees; Establishing a hyper-parameter balance diffusion model; And according to the hyper-parameter balance diffusion model, traversing the parameters beta and Delta in the hyper-parameter balance diffusion model to obtain a personalized recommendation list based on the user and the article. According to the method, the process of inhibiting mass diffusion by using hyper-parameters is provided, so that the diversity is improved as much as possible under the condition that the accuracy is not damaged, an optimal recommendation result is realized by traversing the parameters, and excessive diffusion between crowds and articles is inhibited by simultaneously adjusting popular users and articles; The algorithm is evaluated through the two real data sets, it is proved that the methodis superior to other algorithms in accuracy, diversity and novelty, that is, the model of the method can bring good performance improvement.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation based on big data, and more specifically relates to a method for personalized recommendation based on users and items. Background technique [0002] The development of the Internet and smart mobile devices has made our lives more convenient. Through various network systems, people are gradually accustomed to reading news, watching movies, shopping, making friends and so on online. At the same time, people are also exposed to all kinds of information provided by these websites, and the explosion of information makes it difficult for people to quickly search for objects of their interest on the Internet. The information recommendation system launched based on this situation recommends things of interest to users based on their behavior on the Internet, such as Amazon, Twitter, Taobao, etc. [0003] Due to the impact of information recommendation system on the economy and societ...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06Q30/02
Inventor 王琨王灿刘微卫涛胡有兵金鑫王潇翔叶俊冯佩穆超牛瑞丽龙政强李春雨段金龙卢济孙艳东田俞珍王凯郑建周昱航蔡九鸣丁璇刘贺宋杰王浩健王经纬
Owner XIDIAN UNIV
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