Abstract
Aiming at the shortcomings of traditional recommendation algorithms in dealing with large-scale music data, such as low accuracy and poor real-time performance, a personalized recommendation algorithm based on the Spark platform is proposed. The algorithm is based on the Spark platform. The K-means clustering model between users and music is constructed using an AFSA (artificial fish swarm algorithm) to optimize the initial centroids of K-means to improve the clustering effect. Based on the scoring relationship between users and users and users and music attributes, the collaborative filtering algorithm is applied to calculate the correlation between users to achieve accurate recommendations. Finally, the performance of the designed recommendation model is validated by deploying the recommendation model on the Spark platform using the Yahoo Music dataset and online music platform dataset. The experimental results show that the use of improved AFSA can complete the optimization of K-means clustering centroids with good clustering results; combined with the distributed fast computing capability of Spark platform with multiple nodes, the recommendation accuracy has better performance than traditional recommendation algorithms; especially when dealing with large-scale music data, the recommendation accuracy and real-time performance are higher, which meet the current demand of personalized music recommendation.
Highlights
With the continuous development of network and information technology, music works have exploded and gradually formed a vast music work library
Because there are many conditions to be met for music recommendation, which leads to the traditional single algorithm recommendation with low accuracy, poor real time, and colossal resource consumption, it cannot meet the current demand for a personalized offer of massive music data
Lampropoulou et al [6] proposed a music recommendation system based on SVM to accomplish the query of the same type of music resources through music content retrieval and collaborative filtering algorithm to achieve personalized music recommendation
Summary
With the continuous development of network and information technology, music works have exploded and gradually formed a vast music work library. Tian et al [7] proposed a hybrid LX recommendation algorithm by integrating the logistic regression method and XG-Boost (eXtreme Gradient Boosting). It verified the effectiveness of the LX model by real music dataset, and experiments showed that the method has high accuracy. Erefore, this paper proposes a K-means clustering music recommendation method based on AFSA optimization based on the Spark platform. E collaborative filtering algorithm is used to deal with the similarity between users and improve the clustering effect and recommendation efficiency by the parallel computing capability of multiple nodes of the Spark platform
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
