Research And Implementation Of Mixed Strategy Music Recommendation System Based On Spark

With the rapid development of Internet technology,various music platforms provide a large number of music resources for people to choose.However,with the continuous creation of musicians,the amount of music data is also increasing day by day.It is more difficult for people to find the music they are really interested in.In order to solve the problem of information overload,music recommendation system came into being,which can meet the needs of users by mining the potential information in massive data.Recommendation algorithm is the core of recommendation system.Hybrid recommendation algorithm can effectively solve the limitations of a single algorithm,so as to improve the recommendation efficiency.In order to meet the needs of users in different scenarios,the system provides users with real-time recommendation services and offline recommendation services.In the real-time recommendation part,a model-based recommendation algorithm is adopted.TF-IDF is used to optimize the music similarity matrix generated based on tags,and the optimized music similarity matrix is weighted and mixed with the music similarity matrix generated based on ALS to obtain a new mixed music similarity matrix.This matrix is used to query the music similar to the current music so that it can be more quickly recommended to users when recommending in real time.It not only ensures the real-time performance,but also effectively improves the accuracy of recommendation.In the part of off-line recommendation,aiming at the cold start problem and data sparsity problem in the recommendation system,a dynamic weighted hybrid algorithm based on ALS and the optimized tag based recommendation algorithm is used to calculate the user’s preference for unknown music.Experiments show that the dynamic weighted hybrid algorithm proposed in this paper can better solve the user’s cold start problem and data sparsity problem.By combining the algorithm designed in this paper with spark platform to provide services for users,it can help users solve the problem of difficult choice in the face of massive music.At the end of the paper on the basis of the Spark computing cluster built a relatively complete personalized music recommendation system,and deploy the system test environment,for music recommendation system of each function test,the function of the test results are able to meet expectations,system can also provide users with stable under different scenarios.