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Potential factor-based matrix decomposition completion hybrid recommendation method

A matrix decomposition and factor matrix technology, applied in the computer field, can solve problems such as not being able to meet user needs

Active Publication Date: 2020-06-26
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

Due to the huge amount of data in the era of big data, traditional collaborative filtering recommendation methods can no longer meet user needs

Method used

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  • Potential factor-based matrix decomposition completion hybrid recommendation method
  • Potential factor-based matrix decomposition completion hybrid recommendation method
  • Potential factor-based matrix decomposition completion hybrid recommendation method

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Embodiment Construction

[0063] The technical solution of the present invention will be further described in conjunction with the accompanying drawings.

[0064] combine figure 1 , a hybrid recommendation method based on latent factor matrix decomposition and completion proposed by the present invention, while mixing matrix decomposition and completion, the impact of latent factors on user ratings is considered, and the prediction result is made more accurate by weighting, including the following specific steps :

[0065] S000: Obtain a data set of user item information, construct a user item rating matrix, a user latent factor matrix, and an item latent factor matrix;

[0066] The user item information dataset refers to the user feature information, item feature information, and item collection information related to the user; the user item rating matrix refers to the matrix formed by each user and their ratings on items in the obtained user item information dataset, row Represents items, columns r...

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Abstract

The invention discloses a potential factor-based matrix decomposition completion hybrid recommendation method. The method comprises the steps of 1, constructing a user article scoring matrix, a user potential factor matrix and an article potential factor matrix; 2, calculating and obtaining the similarity between the users and the similarity between the articles; step 3, carrying out matrix decomposition on the user article scoring matrix; carrying out iterative computation on the decomposition matrix to obtain a predicted score of the user on the article; step 4, performing matrix completionon the user article scoring matrix to obtain a prediction scoring matrix and a state value of the corresponding prediction evaluation index; step 5, performing weighting to obtain the prediction scoring matrix and the state value of the corresponding prediction evaluation index; 6, obtaining a final prediction recommendation matrix according to the obtained prediction scoring matrixes and the state values of the prediction evaluation indexes corresponding to the prediction scoring matrixes; and according to the prediction recommendation matrix, finishing recommending articles to the user.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a matrix decomposition and completion hybrid recommendation method based on latent factors. 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 browsing news, watching movies, shopping, socializing and so on online. At the same time, people's behavior habits are also exposed in these websites, and the explosive information makes it difficult for people to quickly and accurately retrieve the content they are interested in on the Internet. With the development of recommendation engines and recommendation algorithms, the way for users to obtain their own information has changed from simple keyword queries to targeted personalized searches. Based on this situation, the information recommendation system launched can Recommend conten...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F17/13G06Q30/06
CPCG06F16/9536G06F17/13G06Q30/0631
Inventor 孙知信杨宏胜孙翌博陈松乐宫婧赵学健胡冰孙哲
Owner NANJING UNIV OF POSTS & TELECOMM
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