Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

Abstract

As an effective solution to the problem of information overload, personalized recommendations have received widespread attention in the music field. A context-aware music recommendation algorithm combining classification and collaborative filtering is proposed based on user context information. Firstly, the similarity analysis of the user situation is carried out. A preliminary list of recommended songs is obtained by collaborative filtering. The machine learning method is used to classify music in different scenes to get the preferences of music types in different situations. Finally, the recommendation list obtained by collaborative filtering is combined with the music type preference obtained by the classification model and personalized music recommendations for users in different situations. This algorithm not only effectively reduces the complexity of the recommendation process. Experiments show that the proposed algorithm can effectively improve the accuracy of users' music recommendations.