Abstract
With the rapid development of the Internet and digital technology, digital music and Internet radio stations have come to attract increasing investment from various sources. Through the Internet, users are able to listen to music and radio stations without geographical limitations. The Internet also provides users more options in their music and radio station choices. The technological developments in this field allow for improvements in car audio systems. However, across all relevant studies, the question of how best to recommend radio or music choices to users remains an important topic in the research relating to digital music and radio. Therefore, in this paper, we try to improve these recommendations by recording user behavior and analyzing the music preferences of users with similar background information. This is different to traditional recommendation mechanisms as we simplify the feedback procedure for users. In addition, by adjusting the weightings in the formula for calculating the recommendations, we are able to provide personalized recommendations. This would better satisfy the needs of different types of users. Through experimental results, we prove that our recommendation mechanism significantly helps users to select radio stations and music.
Published Version
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