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A Hybrid Recommendation Method Based on Autoencoder and Clustering

A technology that mixes recommendation and recommendation methods, applied in neural learning methods, other database clustering/classification, computer components, etc., can solve problems such as not taking into account, and achieve the effect of improving accuracy

Active Publication Date: 2021-09-17
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

However, this method does not take into account the differences between user clusters after clustering

Method used

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  • A Hybrid Recommendation Method Based on Autoencoder and Clustering
  • A Hybrid Recommendation Method Based on Autoencoder and Clustering
  • A Hybrid Recommendation Method Based on Autoencoder and Clustering

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Embodiment Construction

[0062] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0063] like Figure 1-5 The present invention includes the following steps:

[0064] (1) Combining user rating matrix with user demographic characteristics;

[0065] (2) Use the autoencoder to learn user features, and use the obtained user features to cluster users;

[0066] (3) Use MAE to calculate the most suitable recommendation method for each category of users, and combine the recommendation methods to obtain a hybrid recommendation model;

[0067] (4) Calculate the target user category, and use the hybrid recommendation model to get the recommendation result.

[0068] The specific steps of combining the user rating matrix with the user demographic characteristics in step (1) are as follows:

[0069] (1.1) Set the number of users U={U1,U2,...,Un}, the item data set I={I1,I2,...,Im}, the user's rating range for the item is [0,5];

[00...

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Abstract

The invention discloses a hybrid recommendation method based on an autoencoder and clustering. The invention combines user rating data with user demographic data, undergoes normalization processing, and then uses an autoencoder to extract user feature data, and then uses K The ‑means++ clustering method clusters users. When a user has a recommendation requirement, the rating data of the user to be recommended is combined with the demographic data. After normalization, the autoencoder is used to extract the characteristics of the user to be recommended, and then the K The ‑means++ clustering method classifies the recommended users, and finally uses the recommendation method most suitable for the category to recommend the users. The invention makes up for the poor performance of the existing recommendation method on the sparse matrix, and effectively improves the recommendation accuracy.

Description

technical field [0001] The invention belongs to the technical field of feature extraction and recommendation methods, and particularly relates to a hybrid recommendation method based on an autoencoder and clustering. Background technique [0002] With the continuous development of information technology, the information on the Internet has grown exponentially, and users cannot quickly find the information they want on the Internet. This is the problem of "information overload". Both academia and industry are constantly exploring ways to improve the quality of information services to solve the problem of "information overload", and personalized recommendation technology was born. In recent years, according to the needs of different recommendation systems, researchers have proposed corresponding personalized recommendation schemes, such as content-based recommendation, collaborative filtering, association rules, utility recommendation, combined recommendation and so on. [00...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/335G06F16/9032G06F16/906G06N3/04G06N3/08G06K9/62
CPCG06N3/088G06N3/045G06F18/23213
Inventor 朱全银赵阳胡荣林李翔严云洋冯万利周泓王啸瞿学新潘舒新
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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