A method and device for inferring user attributes based on convolutional neural network

Abstract

The invention relates to a method and device for inferring user attributes based on a convolutional neural network. The method establishes a self-centered network according to the attributes and friend relationships of user nodes; then uses a convolutional neural network to extract the hidden information contained in the attribute information and friend relationships of user nodes in the self-centered network, and uses the hidden information to infer The user's missing attributes. For social networks where friend relationships cannot be obtained directly or are difficult to obtain, neural networks are used to classify and predict missing attributes using only user attribute information. The present invention can well avoid the limitation of artificially defining the similarity function, and the convolution operation of the convolution kernel can better show the relationship between different attributes and different attribute dimensions, so as to efficiently and accurately perform User missing attribute inference.

Description

technical field [0001] The invention belongs to the technical field of user missing attribute inference in social networks, in particular to the inference of multi-valued attributes, and in particular relates to a method and device for inferring user attributes based on a convolutional neural network, which has high accuracy. Background technique [0002] With the development of Internet technology, online social products, such as Weibo, Zhihu and Facebook, have become the necessities of users' daily life. While using these products, users generate a large amount of information, including the user's attribute information, post content and friend relationships, which provide data support for enterprises and researchers to accurately describe user portraits. At the same time, in order to protect the privacy of users, online social products provide users with fine-grained privacy settings, which makes it difficult to obtain user attribute information directly. According to rel...

AI Technical Summary

Problems solved by technology

The limitation of the classification-based method is that the calculation of the similarity needs to be comprehensive and accurate, and the accuracy of the classification model must be high, and the construction of data features must be comprehensive. However, although the accuracy of the machine learning model in the current method is high, most of them are aimed The problem of binary classification, and in the social network, it is faced with the problem of not being able to obtain a comprehensive feature vector of multi-dimensional attributes to construct users, which leads to poor prediction results, especially the prediction of multi-valued attributes.

However, the attribute prediction algorithm based on label propagation needs to spend a lot of time to calculate the adjacency matrix of the graph, and the algorithm itself treats the importance of friends of the marked nodes equally, which itself does not conform to the characteristics of social networks, so in real data The effect is also unsatisfactory

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