Addressing the Cold-Start Problem in Content-Based Music Recommendation Systems Through the Implementation of a Weather-Based Music Recommendation System

This study explores a novel solution to the cold-start problem in content-based music recommendation systems by incorporating weather data as a factor for personalized song suggestions. Addressing the challenge of providing accurate recommendations to new users, Music Recommendation System 1 (MRS1) utilizes weather conditions to infer user mood and align music choices accordingly. An R-precision evaluation indicates the effectiveness of MRS1 over the traditional content-based Music Recommendation System 2 (MRS2), despite the research challenges in establishing a direct correlation between weather and mood. The paper underscores the importance of integrating weather factors to enhance user experience and suggests avenues for refining mood categorization and expanding datasets for improved recommendation accuracy. This approach promises to advance personalized music discovery for users without prior interaction histories, enriching the digital music landscape.