Movie recommendation system and method for relieving data sparsity

A data sparse, recommendation system technology, applied in the field of deep learning, can solve problems such as poor accuracy, achieve the effect of improving accuracy and alleviating data sparsity

Pending Publication Date: 2020-10-16
中山大学新华学院
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a movie recommendation system and method for alleviating data sparsity in

Method used

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  • Movie recommendation system and method for relieving data sparsity
  • Movie recommendation system and method for relieving data sparsity

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Experimental program
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Effect test

Embodiment 1

[0030] Such as figure 1 As shown, a movie recommendation system that alleviates data sparsity includes an initial data input module, a data processing module, and a recommendation list module; the initial data input module includes a movie profile data input submodule, a movie review data input submodule, and actual rating data Input submodule and crew information data input submodule;

[0031] The movie profile data input submodule and the actual score data input submodule are merged with the improved convolutional neural network in the data processing module, and then transmitted to the recommendation list module; the crew information data input submodule is in the data processing module A mathematical model is established in the module, and then the recommendation list module is embedded; the movie review data input sub-module is processed by the AFINN algorithm in the data processing module and then transmitted to the recommendation list module.

[0032] In the above scheme, th...

Embodiment 2

[0038] Such as figure 2 As shown, a movie recommendation method for alleviating data sparsity, applied to a movie recommendation system for alleviating data sparsity, includes the following steps:

[0039] S1: The movie profile data input sub-module is merged with the improved convolutional neural network to obtain the movie feature vector matrix;

[0040] S2: The movie feature vector matrix is ​​mixed with the actual rating data input submodule to obtain a mixed rating matrix;

[0041] S3: The original recommendation model improves the matrix decomposition limit and forms a neural network model;

[0042] S4: Construct a new recommendation list module through the fusion of the hybrid scoring matrix and the neural network model.

[0043] In step S1, the convolutional neural network includes an embedding layer, a convolutional layer, a pooling layer and an output layer. When the convolutional neural network is fused with the movie profile data input submodule and the actual rating data ...

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Abstract

The invention relates to a movie recommendation system for relieving data sparsity. The movie recommendation system comprises an initial data input module and a recommendation list module, the initialdata input module comprises movie introduction data, movie comment data, actual scoring data and theater group personnel information data; the movie introduction data and the actual scoring data areprocessed by an improved convolutional neural network and then transmitted to a recommendation list module; establishing a mathematical model according to the theater group personnel information data,and embedding the mathematical model into the recommendation list module; and the movie comment data is processed through an algorithm and then transmitted to a recommendation list module. The invention provides a movie recommendation system and method for relieving data sparsity. The movie introduction data, the movie comment data, the actual scoring data and the theater group personnel information data are collected, the improved convolutional neural network and the mathematical model are utilized to perform movie information fusion, the new recommendation list is generated, the accuracy ofthe system is improved, and the problem of data sparsity of the user-scoring matrix is relieved.

Description

Technical field [0001] The present invention relates to the field of deep learning, and more specifically, to a movie recommendation system and method for alleviating data sparsity. Background technique [0002] The rapid development of the Internet and information technology has not only provided convenience to people, but also produced massive amounts of data. The most direct way for users to quickly extract the information they are interested in from the massive amount of information is search engines and recommendation systems. Search engines must accurately input keywords, and the recommendation system is based on similarity recommendations between users and product items. [0003] At present, the most commonly used algorithms in recommendation systems can be roughly divided into the following categories: 1. Content-based recommendation algorithm, that is, recommend based on the similarity between products; 2. Knowledge-based recommendation method can map user needs to produc...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06K9/62G06N3/04G06N3/08G06F40/284G06F40/205
CPCG06F16/9535G06N3/08G06F40/284G06F40/205G06N3/045G06F18/25
Inventor 黎丹雨陈怡华徐艳梅丁阳
Owner 中山大学新华学院
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