Proposal of Classified Music Recommendation Model Based on Social Media

Abstract

With the spread of smartphones, it has become common to always listen to music like background music, so it is necessary to create a music database that meets individual needs. By creating a music database using data from social media, music is classified in a different way from collaborative filtering, which is mainly used by existing music source providing platforms. Various other hashtags attached to posts with music titles used as hashtags are collected using web crawling, and music is classified based on the collected hashtags to reflect actual listeners’ opinions on music. It uses crawling to find posts on social media with music titles tagged as hashtags, then collects other hashtags attached to those posts. Hashtags collected by performing the same task with multiple music titles are collected, analyzed, and statistics are then classified to determine when and where the music fits. On social media, the feelings of the person who posted the post and the conditions such as the time zone, place, weather, and situation where the post was posted are reflected as hashtags. By analyzing the hashtags attached to the music title, it is possible to build a social media-based music database in which the opinions of various people are reflected as collective intelligence. It is possible to derive different results from existing collaborative filtering based on the past listening records of users of the platform used by most of the sound source providing platforms. Even if music titles are not written in complete form on social media hashtags, we plan to research them so that they can be used to build a database.