<![CDATA[The rapid growth of the digital era has led to an increase in online music platforms and music users. However, this abundance of choices has resulted in information overload, making it challenging for users to find their favorite music easily. Thus, the aim of this study was to propose an effective music recommendation method that considers user attributes, music genres, and temporal dynamics. The research utilized a collaborative filtering approach, leveraging user data and music preferences to generate relevant recommendations. Thus, the proposed method aimed to address the issue of information overload and provide more personalized and accurate music recommendations. The results of the recommendation experiments demonstrated the positive effects of integrating these three perspectives. The recommendations generated from this approach were able to assist users in finding their preferred music more conveniently, thereby enhancing user satisfaction with online music platforms. ]]>
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