Research On Personalized Music Recommendation Algorithm Based On Collaborative Filtering

This paper is mainly aimed at the object of music,aiming at providing personalized music recommendation for users,It is improved on the basis of traditional collaborative filtering recommendation algorithm,and achieves the goal of providing personalized music recommendation for users by integrating collaborative filtering based on users attributes on the basis of user based collaborative filtering.Then,the experimental design and results show that the improved recommendation algorithm can improve the effect of personalized recommendation,and the accuracy of user recommendation is higher.The current popular recommendation algorithms have their own advantages and disadvantages.Among these popular recommendation algorithms,collaborative filtering recommendation algorithms have received extensive attention due to their maturity.However,traditional collaborative filtering usually has unavoidable problems such as cold start,data sparsity and scalability.Therefore,in order to effectively curb the drawbacks caused by data sparsity,this paper improves the calculation method of user similarity in the basic user collaborative filtering,that is,on the basis of the traditional calculation method of jaccard similarity.Although the traditional jaccard similarity takes into account the impact of user evaluation on the similarity,it does not take into account the impact of users’ actual scores on the similarity.Specifically,the similarity calculation result is not limited by the score.Therefore,in order to limit the calculation of similarity due to different user ratings,different user ratings are added to the similarity calculation formula.Then,for the object of music,considering the similarity and difference of users’ own attributes on preferences,consider integrating the gender and age attributes of user attributes into the recommendation algorithm,and obtain similar users through the similarity calculation of gender and age attributes,so as to improve the recommendation accuracy.Finally,the time factor of collaborative filtering based on user and user attributes is compared.Through experimental verification,this paper,based on the traditional collaborative filtering recommendation algorithm,is feasible to mix it with the weighted collaborative filtering recommendation of user attributes.First,establish the user similarity model,Then the data set of Last.fm is substituted into the experiment for testing.By comparing the recall,accuracy and average absolute error of the traditional recommendation algorithm and the algorithm proposed in this paper,It is verified that the new algorithm that combines user collaborative filtering and user attribute weighting is compared with the recommendation results of traditional recommendation algorithms,With higher accuracy,And the combination of time factor has better recommendation effect.