The Investigation of Music Recommendation Systems

Abstract

A recommendation system represents a machine learning technique that examines and presents user preferences derived from their online activities. In today's digital landscape, recommendation systems find widespread use in short video platforms. These systems scrutinize user actions such as search history and viewing patterns to proactively suggest videos aligned with individual tastes. Leveraging an effective recommendation system to propose high-quality videos tailored to user preferences proves advantageous in sustaining user engagement and boosting platform traffic. Nonetheless, users with specialized interests or newcomers to the platform may receive less accurate recommendations due to limited data availability. The development of an artificial intelligence-based recommendation system can significantly expedite product searches, enhance search precision, foster user loyalty, augment average order values, and bolster overall conversion rates. In the realm of artificial intelligence applications, recommendation systems have become indispensable components. This paper delves into the algorithms for automatically analyzing and recommending music based on users' historical playback records. The study employs a random forest model to train the recommendation algorithm.