Application of Hybrid Recommendation Algorithm Based on Collaborative Filtering and Content in Cloud Vocal Music Teaching
ShenghuanZhang, Lihong Xu
International Journal of Web Applications, Vol. 16, No. 4, 2024.
DOI: https://doi.org/10.6025/ijwa/2024/16/4/143-150
Link: https://www.dline.info/ijwa/fulltext/v16n4/ijwav16n4_4.pdf
Abstract:
This article aims to introduce a new type of cloud music education platform which
can meet the current high demand for music education while providing convenient,
economical, and personalized services. The platform uses the Scrapy crawler framework,
Web frontend, and Laravel framework and achieves efficient services through
data interaction and embedded development. Using a hybrid recommendation algorithm,
we can more accurately estimate the association between users and specified
objects and decide whether to use this method by comparing their similarities.
Through this work, we have achieved significant results in building a complete cloud
music course experience, including a prediction accuracy of 8.41%, a recommendation
effect of 10.21%, and a preference classification prediction accuracy of 2.55%.