Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (5): 1845-1850.doi: 10.13229/j.cnki.jdxbgxb20200243

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Music personalized recommendation algorithm based on k⁃means clustering algorithm

Hai-long WANG(),Lin LIU(),Min LIN,Dong-mei PEI   

  1. College of Computer Science and Technology，Inner Mongolia Normal University，Hohhot 010022，China
  • Received:2020-04-16 Online:2021-09-01 Published:2021-09-16
  • Contact: Lin LIU E-mail:wanghailong5256@163.com;wangqiuting232@163.com

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Abstract:

In order to pursue higher recommendation quality and faster recommendation efficiency， a personalized recommendation algorithm based on Mir and k-means tag clustering is constructed with music resources as the research objective. The pitch change and contour are selected as the system processing data， and the input， preprocessing， feature extraction， similarity matching and output modules are used to construct the music information retrieval system. According to the selection relationship between users and resources， a multi-mode network is formed. The dynamic multi-dimensional network model of the retrieval system is established through the extension， local range definition and connection priority stages Tag clustering searches neighborhood users to obtain the nearest neighbor user set. The comprehensive eigenvalue is set as the initial clustering center， and personalized recommendation is realized according to the sorted recommendation resource prediction results. The performance of the algorithm is verified by the average absolute error， accuracy rate and recall rate. After the comparison of experimental data， it is found that the accuracy of the proposed algorithm is ideal， the error is small， and the recommendation effectiveness is superior.

Key words: music information retrieval, k-means algorithm, label clustering, pitch contour, personalized recommendation, similarity

CLC Number: 

  • TP393

Fig.1

Schematic diagram of MIR processing flow"

Table 1

Music scoring mechanism"

喜爱程度评分判定标准
最喜欢5标记为红心
不讨厌3听完未做标记
不喜欢2播放下一首
讨厌1标记为不再推荐

Table 2

Statistics table of data sets of different sizes"

指标100 kbit1 Mbit10 Mbit
评分数量/条95 4281 036 57311 873 482
用户数量/人9 3575 823713 578
音乐数量/首1 8824 327112 837

Table 3

Data set file format statistics table"

文件名称数据种类格式
rating.dat评分数据User ID：：Music ID：：Rating：： Time stamp
music.dat音乐数据Music ID：：Title：：Genres
user.dat用户数据User ID：：age：：gender：：occupation：：zip code

Fig.2

Comparison of experimental data of performance indicators of various algorithms"

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