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Method of Dynamically Personal Customized Music Categorization Service Based on Real-Time Social Music Consumption Analysis

IP.com Disclosure Number: IPCOM000241274D
Publication Date: 2015-Apr-12
Document File: 2 page(s) / 77K

Publishing Venue

The IP.com Prior Art Database

Abstract

A main idea defines a method of dynamically personal customized music categorization based on real-time social music consumption analysis for improving music streaming service. The method can collect user background, receive detected scene and location where the selected music is playing, calculate music preference weight for certain culture and user group, and provide a dynamically updated personal customized music categorized list.

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-Time Social Music Consumption Analysis

Time Social Music Consumption Analysis

    Online music streaming service is hot area. Those music streaming service providers are trying to organize their music collection to satisfy all customers with different music classification methods (such as style, genre, location, or purpose based), but it seems an endless work due to 25 billion songs in the world.

    There are a lot of different music classification methods used by streaming music providers. For instance, there are style based, genre based, location based, or purpose based categorization systems, but music lovers want easily to find a right type of music in right time. There is no an universal music classification method to fit all different needs from different user groups. It is normal that a same song can be classified into different categories in different regions, vendors, or libraries. Users from different cultures and groups have different music categorization conventions. Both online music streaming service providers and users want to find a best music classification way to find and select a right music for a right purpose in a right time. There is no ideal automated method to maintain and update a music selection list based on users culture and using background in real time. Therefore, it is necessary to define a method of dynamically personal customized music categorization based on real-time social music consumption analysis for improving music streaming ser...