Information recommendation method and system based on convolutional neural network and noise reduction auto-encoder

A technology of convolutional neural network and autoencoder, which is applied in the field of information recommendation based on convolutional neural network and denoising autoencoder, can solve problems such as limiting algorithm improvement, difficulty in feature extraction, and difficulty in learning high-level information

CN111127146AActive Publication Date: 2020-05-08JIANGXI UNIVERSITY OF FINANCE AND ECONOMICS
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2020-05-08

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Abstract

The invention discloses an information recommendation method and system based on a convolutional neural network and a noise reduction auto-encoder. Two deep learning models of a convolutional neural network and a noise reduction auto-encoder are used to learn user preferences from scores, trust, comments and other data. Meanwhile, a new correlation regularization method is provided to establish relationships of user preferences in different aspects, so that the performance is improved. Firstly, compared with a previous model, rich comment information is fused; then, preliminary processing is conducted on the comment text through a convolutional neural network model, the extracted effective features are put into a noise reduction auto-encoder model to extract hidden features of the commenttext, and more effective and compact representation of the comment text is obtained; finally, two noise reduction auto-encoders are added and used for processing scores and trust information respectively, corresponding prediction vectors are obtained through the three noise reduction auto-encoders respectively, weighted fusion is carried out, and therefore user preferences are modeled more accurately.
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Description

technical field

[0001] The present invention relates to the technical field of information recommendation, in particular to an information recommendation method and system based on a convolutional neural network and a noise reduction autoencoder. Background technique

[0002] In recent years, recommender systems have been widely used in various industries. According to the user's needs and interests, the recommendation system mines the items that the user is interested in from the massive data through the recommendation algorithm, and recommends the results to the user in the form of a personalized list. Personalized recommendations are one of the key applications of machine learning in areas such as e-commerce. Many recommender systems use collaborative filtering methods to make recommendations. Although many recommendation algorithms have been proposed in the recommendation field, there are still some well-known problems, such as data sparsity and cold start, etc. In re...

Examples

Embodiment Construction

[0062] In this embodiment, an information recommendation method based on a convolutional neural network and a noise reduction autoencoder is taken as an example, and the present invention will be described in detail below in conjunction with specific embodiments and accompanying drawings.

[0063] see figure 1 , figure 2 and image 3 , showing an information recommendation method and system based on a convolutional neural network and a noise reduction autoencoder provided by an embodiment of the present invention.

[0064] In the recommendation system of the embodiment of the present invention, it is assumed that there is a set U={u of M users 1 , u 2 ,...,u M} and a set of N items I={i 1 ,i 2 ,...,i N}. Score, trust, and comment data usually imply the user's potential preference information, and are the main data used by the recommendation algorithm. User-item rating matrix R M×N ={r u,i} represents the ratings of all users on all items, where r u,i Indicates use...