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The personalized distance function forms the basis for generating the personalized interface.
After recording the user's perception of distances through surveys, the above formula is utilized to obtain the personalized distance function.
In this paper, we design a mechanism to build a personalized distance function for each user.
The system employs the personalized distance function to cluster tracks using the SOM.
The competitive learning procedure of the SOM with a personalized distance function is as follows [34]: Step 1: Initialization.
The system first utilizes the Internet to estimate the user's perception of 'music distance', which will then be used to generate a personalized distance function.
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In addition, the variation in the multi-dimension parameters for each user's distance function is quite large, indicating the necessity of personalizing the distance function.
Therefore, a method to personalize the distance formula is proposed.
Personalize your cape.
Personalize your space.
Personalize the space.
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