Online user type identification method and system based on visual behavior

Abstract

The invention discloses an online user type identification method and an online user type identification system based on visual behavior. The online user type identification method based on the visual behavior includes: collecting and processing eye movement data of one type or various types of users, obtaining a watching information data set and a user type set, obtaining a piece or multiple pieces of eye movement feature data according to watching information concentrated in the watching information data set so as to generate a sampling data set, selecting a part of the eye movement feature data from the sampling data set, inputting the selected eye movement feature data into a support vector machine, obtaining a user type classifier through training, completing a machine learning process so as to obtain a classifier, inputting collected eye movement data of each arbitrary online user into the user type classifier after being trained, and identifying a user type of each arbitrary online user according to the obtained classifier. The online user type identification method based on the visual behavior proactively uses an eye movement tracking technology to obtain and calculate three types of the eye movement feature data of each user when each user browses a webpage, and judges the types of the online users according to differences of the eye movement feature data. The online user type identification method and the online user type identification system based on the visual behavior perform user identification based on the visual behavior, can proactively record the eye movement data of the online users, simply and conveniently extract the data, and are high in accuracy and reliability.

Description

technical field [0001] The invention relates to the technical field of automatic user type identification, in particular to a visual behavior-based online user type identification method and system. Background technique [0002] With the development of science and technology and the popularization of the network, the network has become an indispensable communication tool and information exchange platform for people's life, study and work. Information requests are slow to receive manual input from users, but users can quickly obtain a large amount of information from computer interfaces and audio, which will cause a problem of unbalanced human-computer interaction bandwidth. With the widespread use of computer networks and the increasing standards of public demand, the research on computer network intelligence has attracted extensive attention. [0003] Network intelligence not only requires information processing intelligence, but also human-computer interaction intelligenc...

AI Technical Summary

Problems solved by technology

[0005] The existing online user type identification is mainly through questionnaires, online click-through rate and other methods, so it is difficult to obtain the psychological activities of online users in the process of surfing the Internet, the identification accuracy is low, and the credibility is not high

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