Music Recommendation System using Hybrid Approach

Due to the expansion of the World Wide Web a lot of music data is now accessible online. It becomes a requirement to create a suggestion service in addition to conducting searches for user’s anticipated musical things. A Music Recommendation System using a hybrid approach is presented in this paper, which provides a personalized music recommendation service by using a combination of several factors. A hybrid approach is a powerful tool in the development of music recognition, as it can classify music into different genres based on the audio signal beats. This allows for easier categorization and understanding of the similarities between various music genres. Moreover, this approach could be further developed to improve the accuracy of automatic music classification. By using the hybrid approach, music recognition research can become more efficient and accurate, resulting in an advancement of the field. In a personalized music recommendation System, to integrate the results of the hybrid algorithm with the log files, this study has used the recommended method of collective filtering (CF). To make music recommendations to the user, this algorithm was created, based on their individual preferences. The CF recommendation algorithm takes as input the user's favorite songs and the log files Every user who uses a system for tailored music recommendations has their history recorded in the log file. The system that provides tailored music recommendations takes the user's history from the log file and suggests songs for each genre. Giving the user with positive human-computer interaction and top-notch recommendations is the primary goal of music recommendation. And to achieve this, the user interface allows users to play songs and simultaneously recommends songs based on the current song being played. The audio features and lyrics scores of each pair of songs are fed into the algorithm for analysis. The suggestions are stored using firebase storage, which ensures that the user gets reliable recommendations.