Visualization of data analysis platform Taking QQ music recommendation system as an example

Abstract

With the rapid development of big data technology, people's demand for personalized music recommendation systems is growing more and more urgent. However, the current music recommendation system still has some problems, such as inaccurate recommendations and too slow recommendation speeds, as well as cold starts and data sparsity caused by massive data. In order to design and implement a music recommendation system for the recommendation system storage caused by the continuous increase of data, insufficient storage, and computing power, this paper improved the QQ music recommendation system based on the collaborative filtering recommendation algorithm of the offline data warehouse technology project. After testing, the music recommendation system designed in this paper has good stability, scalability, and efficiency.