Method of deducing structural attributes of online social network users

Abstract

The present invention discloses a method of deducing the structural attributes of the online social network users. The method comprises the steps of coding a plurality of attributes of the users into the structural combined attribute category vectors, carrying out the weighted random walk in a user node relational graph G to obtain a user node sequence set, then utilizing a word-to-vector tool Word2Vec to generate the real-value vector representation of each user node, and constructing a full-connection neural network model to train, when the user attributes are deduced, inputting the user node vector representation of which the attribute needs to be deduced in the trained neural network model, calculating the probability of each combined attribute category vector, and taking the vector having the maximum probability as the combined attribute category of the user. According to the present invention, the attribute information of partial users and a friend relation (or a concern relation) between the users just need to be extracted, the additional user behavior feature data does not need to be obtained, at the same time, the provided method fully utilizes the internal relation between the attributes and enables the attribute deduction accuracy to be improved.

Description

technical field [0001] The invention belongs to the technical field of network information, and more specifically relates to a method for inferring structured attributes of online social network users. Background technique [0002] Attribute inference is the main technical method used to automatically predict the unknown attributes and potential characteristics of users in online social networks. It can be applied to define different customer types in market analysis, and deeply mine user attribute information to optimize personalized recommendation methods. Possible user attribute inference attacks take corresponding protective measures. Such as guessing the user's age, gender, geographical location, interest, occupation, etc., this information can be used for product recommendation and information recommendation. [0003] The features extracted by existing attribute inference methods include personal information of users such as gender, age, occupation, and education leve...

AI Technical Summary

Problems solved by technology

Although the existing attribute inference methods have completed the task of attribute inference, they require more input information, and it is difficult to avoid the influence of false user information on the attribute inference results, so the portability of attribute inference is not high.

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