基于用戶行為數(shù)據(jù)的非負(fù)矩陣分解音樂軟件推薦算法研究

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中圖分類號(hào)：TP391.4 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-4706（2025）08-0111-06

Abstract： With the popularity of intermet music services，how to accurately recommend music for users has become an importantresearch topic.This paperaimsattheshortcomingsof theexistingmusicrecommendationsystemindealingwith problemssuchascold-startanddatasparsity.AmusicrecommendationalgorithmbasedonNon-NegativeMatrixFactorization （NMF） is proposedThe studyusesadataset fromacolaborationproject with NetEaseCloud Music，whichcontains more than 57 millon music interactionrecordsofmore than2millonusers.Byintroducinguserbehavior weightsandsparseconstraints， weighted NMFand sparse NMF models are constructed respectively.The experimental results show that the weighted NMF performs best when dealing with high-frequency interactive users，and the F1 score reaches .The sparse NMF has more advantages in dealing withcold-start users.Forusers with fewer than1O interactions，therecommendation accuracy is 1 5 % higher than that of the basic NMF.The research results provide new solutions for the optimization of the music recommendation system.

Keywords：Machine Learning; music recommendation model; NMl

0 引言

隨著互聯(lián)網(wǎng)和信息技術(shù)的快速發(fā)展，數(shù)字音樂產(chǎn)業(yè)得到了迅猛擴(kuò)展。（剩余8976字）