Research on music culture personalized recommendation based on factor decomposition machine

The emergence of Internet music has slowed down the restrictions of space and time on people’s enjoyment of music information services. However, in the face of massive and growing music works, information overload has become the most direct problem, and the need to improve user experience has become very urgent. One of the effective solutions to information overload is recommender system, which can help people to discover the interesting content from the complicated information. Therefore, the combination of recommendation system and Internet music has become an inevitable trend of music development. Referring to the traditional music recommendation methods, this paper proposes a big data music personalized recommendation method based on big data analysis, which combines user behavior, behavior context, user information, and music work information. In this paper, the user big data is introduced into the model building process. Through the factor decomposition machine (FM) learning method, the effect of various influencing factors on user behavior is analyzed to build the user dynamic interest model and complete the user preference acquisition. In the stage of recommendation candidate set selection, combining with the traditional collaborative filtering recommendation idea, the work of recommendation candidate set selection is carried out from two aspects. At the same time, this paper designed and completed a comparative experiment with the processing performance, accuracy and coverage as indicators, and verified the effectiveness of the improved recommendation method.