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

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.

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...

AI Technical Summary

Problems solved by technology

Secondly, some models only integrate one or two types of information, facing the problem of data sparsity, which may limit the improvement of the algorithm and make it difficult to use deep models to learn high-level information from data such as trust and rating

Finally, another challenge is the problem of data sparsity

Some scholars use content-based recommendation or hybrid recommendation to alleviate the problem of data sparsity, but there are problems such as difficulty in feature extraction

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