Application And Research Of Personalized Music Recommendation System Based On User Preference

With the explosive growth of information,network resources are facing the problem of information overload.Facing the massive music resources on the Internet,it is difficult for people to quickly find music that matches their own interests.In order to enable users to effectively obtain the required music information,a music recommendation system came into being,and its appearance can help users quickly find the music they want.This personalized recommendation service can provide users with a better experience and have commercial advantages,so the field of music recommendation has also become a very important research direction in the Internet industry.As the music recommendation system is concerned,when the user performs a period of operation in the system,a large amount of explicit and implicit behavior data will be generated.Many existing personalized music recommendation systems ignore these user behavior data,making it impossible for users to find music with similar listening preferences according to their own behavior habits,reducing the user experience.Based on the above problems,we focuses on the potential relationship between user behavior data and data set data and the design of music recommendation methods by analyzing mainstream music recommendation systems at home and abroad,and on this basis,a personalized music recommendation system based on user preferences is implemented,The main research results of this article are as follows:1.Analyze the mainstream music recommendation algorithm and complete the data set construction.In order to find a method for constructing a user preference model,a comparative analysis of mainstream music recommendation algorithms is carried out,focusing on research on the required data set,combined with consideration of preference characteristics and other factors,and adopting the method of building a music data set by itself will have music characteristics The attribute of Net Ease cloud music playlist data is used as the system data set.First analyze the front-end data of the website,use the crawler tool to obtain the detailed data of the song list created by the user on the Internet,and preprocess the data according to the obtained results.The obtained results are used as the basis for establishing the user preference model.2.Theoretical analysis of user preference models and information,comparison of existing music recommendation systems,and two recommendation algorithms combining preferencefeatures and neighbor user models are proposed.The former establishes a user preference model by merging user implicit data and the similarity value of music in the song list to make a recommendation,and the latter modifies the similarity threshold in real time according to the user preference similarity to find neighboring users,and calculates the user similarity to produce a recommendation result.The data generated by the two recommendations is weighted and fused to achieve the purpose of mixed recommendations.Combining music and song list label data,the recommendation of new users is realized through the association between the user preselected label and the music label.Finally,through experiments,it is concluded that the accuracy and recall of this hybrid recommendation method are superior to traditional recommendation algorithms.3.By combining the actual requirements of the music recommendation system,based on the analysis of the system process and the determination of the core algorithm,the requirements analysis,architecture design,database design,and function module design of the recommendation system are designed and implemented.Music recommendation system.The system implements data collection,processing and storage,user interaction and other modules,making it more complete on the basis of music recommendation.