Article score prediction method based on improved graph convolutional neural network

Abstract

The invention relates to an article score prediction method based on an improved graph convolutional neural network, and belongs to the technical field of information recommendation. The method comprises the following steps: S1, acquiring a historical score of a user on an article, personal information of the user and attribute information of the article; S2, constructing a user-article relationship graph, a user-user relationship graph and an article-article relationship graph; S3, extracting the structure and content features of nodes in the plurality of relational graphs by using an improved graph convolutional neural network; S4, selecting multiple pieces of feature information of fusion nodes of the neural network model; S5, predicting the probability that the user is interested in the article according to the feature representation of the user and the article; S6, training a model by using the training set and the verification set; S7, utilizing the trained model to predict the score of the user on the article. According to the method, the features of the user and article relation graph are effectively extracted through the improved graph convolutional neural network, the score of the user to the article is predicted, and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of information recommendation, and relates to an item rating prediction method based on an improved graph convolutional neural network. Background technique [0002] With the rise of e-commerce and social media platforms, recommendation systems have become an indispensable part of modern artificial intelligence. In the era of information overload, providing users with personalized and highly accurate recommendation services is an important cornerstone for improving business benefits. By analyzing the data left during the interaction between users and the Internet, capturing user preferences and recommending products for users is the main goal of recommendation. How to effectively extract user preferences from diverse data is a key link. [0003] Deep learning plays an important role in recommender systems due to its powerful representation ability, among which, novel deep learning models dealing with graph-...

AI Technical Summary

Problems solved by technology

[0004] However, many current methods only consider the single relationship between users and items, and simply use the structural information between data to simulate message dissemination, resulting in inaccurate recommendation information and little usability for users.

Images