A method for reducing a Mai Chi effect in a movie recommendation system

A technology of Matthew effect and recommendation system, applied in digital data processing, special data processing applications, instruments, etc., can solve the problem that users are difficult to find items of interest, reduce the coverage and accuracy of recommendation systems, increase popular items and Issues such as non-popular items

Active Publication Date: 2019-03-01
CHONGQING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0003] The Matthew effect, that is, the so-called effect that the strong are stronger and the weak are weaker. If it is a recommendation system, it will increase the popularity gap between popular items and non-popular items, making the popularity develop in a polarized trend, making it difficult for users to find out what they really are. Items of interest, reducing the coverage and accuracy of the recommendation system, which will have a very negative impact on the recommendation system
The current mainstream recommendation algorithms all have the Matthew effect

Method used

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  • A method for reducing a Mai Chi effect in a movie recommendation system
  • A method for reducing a Mai Chi effect in a movie recommendation system

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Embodiment Construction

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0030] Such as figure 1 As shown, the present invention discloses a method for reducing the Matthew effect in a movie recommendation system, comprising the following steps:

[0031] S1. Obtain a movie rating matrix and a movie popularity set, the movie rating matrix includes the ratings of each movie by each user, and the movie popularity set includes the popularity of each movie;

[0032] S2. Acquire movies affected by the Matthew Effect based on the movie scoring matrix and the movie popularity set, and establish a set of movies affected by the Matthew Effect;

[0033] S3, calculating the popular periodic function and the standard popular periodic function of each movie in the movie set affected by the Matthew effect;

[0034] S4. Based on the popular periodic function and the standard popular periodic function of each movie in the Matthew effect affected...

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Abstract

The invention discloses a method for reducing a Mai Chi effect in a movie recommendation system. The method comprises the following steps of obtaining a movie scoring matrix and a movie popularity set; establishing a Matthew Effect influence movie set; calculating the epidemic periodic function and the standard epidemic periodic function of each movie in the movie set influenced by the Matthew Effect; based on the epidemic periodic function and the standard epidemic periodic function of each movie in the Mai effect influence movie set, calculating a residual quadratic sum of each movie in theMai effect influence movie set, and establishing a residual set; and based on the residual set, punishing the average score of each movie in the Matthew Effect influence movie set to obtain a new average score of each movie in the Matthew Effect influence movie set, thereby reducing the Matthew Effect in the movie recommendation system. By adopting the method disclosed by the invention, the Mai Chi effect in a movie recommendation system can be reduced, and the situation that hot movies have too high popularity for a long time and articles that are not popular have no interrogation is prevented.

Description

technical field [0001] The invention relates to the field of movie recommendation systems, in particular to a method for reducing the Matthew effect in the movie recommendation system. Background technique [0002] The recommendation system is a tool to help users quickly discover useful information. It is mainly characterized by modeling the user's interest by analyzing the user's historical behavior, so as to actively recommend information to users that can meet their interests and needs. The movie recommendation system is one of the important components in the field of recommendation systems. It collects information about users and movies to help users quickly find movies that interest them in a huge movie library. A good recommendation system not only requires High recommendation accuracy and recommendation coverage should also have better performance in anti-cold start ability and ability to overcome matrix sparsity. [0003] The Matthew effect, that is, the so-called ...

Claims

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Application Information

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IPC IPC(8): G06F16/9535
Inventor 何波裴剑辉
Owner CHONGQING UNIV OF TECH
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