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- W2893578182 abstract "Nowadays, online advertising is very famous and evolving over the past decades that users feel that it is annoying and wasting of time to view the advertisements. Presently, technologies have improvise to include data mining technique that is able to study your preferences and behaviors to predict and provide advertisements that suit the users’ needs. The effectiveness of advertising is high due to the system able to gather data behaviors and provide advertisements based on the users’ needs. A multi-criterion statistical model is established to provide user with closest possible matches of advertisements. The proposed model identifies the returning users based on various aspects of behaviors as well as previous session to identify the type of user that previously recorded in the system. Then, the information for the behaviors are collected and then classified into two groups; users’ behaviors, users’ preferences, and survey to obtain top three categories based on the frequencies of revisiting for an advertisement. As a result, the accurate advertisements will be displayed to the online users." @default.
- W2893578182 created "2018-10-05" @default.
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- W2893578182 creator A5039048507 @default.
- W2893578182 date "2018-01-01" @default.
- W2893578182 modified "2023-09-23" @default.
- W2893578182 title "Multi-criterion statistical model for online advertisement system" @default.
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- W2893578182 doi "https://doi.org/10.1063/1.5055470" @default.
- W2893578182 hasPublicationYear "2018" @default.
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