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A Recommendation Method Incorporating User Curiosity Mechanism

A recommendation method and curiosity technology, applied in special data processing applications, instruments, calculations, etc., can solve problems such as user resistance, and achieve fast fitting speed, personalized recommendations, and high diversity.

Active Publication Date: 2021-07-20
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, in the actual application scenario, if too many items similar to the historical records are recommended to the user, the user will have a sense of resistance

Method used

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  • A Recommendation Method Incorporating User Curiosity Mechanism
  • A Recommendation Method Incorporating User Curiosity Mechanism
  • A Recommendation Method Incorporating User Curiosity Mechanism

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Embodiment Construction

[0060] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0061] A recommendation method that incorporates user curiosity mechanisms, such as figure 1 shown, including the following steps:

[0062] Step 1: In the user data without friend relationship, use the k-nearest neighbor algorithm to find implicit friends. In the historical data of each user, for implicit or explicit friends, use the Pearson correlation coefficient to calculate the user and friend The similarity between each user's friends is sorted from large to small according to the similarity.

[0063] Each user data can be composed of (u, i, r, t) tuples, that is, user u scores r for item i at time t, and U and I refer to the collection of users and items respectively. |U|=m, |I|=n represent the number of users and items in the data set, respectively.

[0064] St...

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Abstract

The invention discloses a recommendation method that integrates the user's curiosity mechanism, including the following steps: calculating the novelty degree and the conflict degree according to the historical records, and then obtaining the stimulation degree through weighted summation, and training each user according to the stimulation degree list of the historical records Respective Wundt curves; use the accuracy-based recommendation method to learn to obtain a list of correlations; calculate the curiosity of the user according to the Wundt curves, sort them, and get each user's curiosity list; finally use wave Sorts two lists using up counting.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to a recommendation method incorporating a user curiosity mechanism. Background technique [0002] With the development of the Internet, the Internet generates a large amount of data all the time, and most of these data are messy and disorderly. It is difficult for users to directly extract the information they want from these data. This problem is the problem of information overload. . This is a question of concern to the whole world. After years of research, the technology to extract important information from these information has gradually matured, and one of the most important technologies is the personalized recommendation technology. Personalized recommendation technology can recommend information that the user is interested in to each user through the user's interests, characteristics, and historical records. Personalized recommendation technology play...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535
Inventor 许可莫俊文闵华清蔡毅
Owner SOUTH CHINA UNIV OF TECH
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