User Preference Analysis and Visualization through the Browser History of Smart Devices

This paper studied the analysis of user preference and visualization through the browser history of smart devices. For the analysis of user preference, the browser history data which the user produced through smart devices in everyday life were automatically collected and transmitted to the server. Then using a web crawler the appropriate web pages were collected then classified by topics through a mechanical learning algorithm, and analyzed to determine which topic and category in the contents the user preferred. As a result of the experiment, the application of the Naïve Bayes algorithm yielded the best classifying capacity. And this experiment delivered the data through visualization to provide the user or the service provider reference. Through this test the experiment could classify the user character according to the consumption of contents by the smart device user, and understand the preference of the contents by the type of the user. Also, it could be utilized as an essential technique to provide personalized recommendation.