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"

1 Minerva B B, Dolores G R, Isidora F F, et al. Use of new technologies by adolescents in the search for health information[J]. Atencion Primaria, 2018, 50(9): 547-552.
2 Jia M, Komeily A, Wang Y, et al. Adopting internet of things for the development of smart buildings: a review of enabling technologies and applications[J]. Automation in Construction, 2019, 101(5): 111-126.
3 Im H, Song H, Jung J. The effect of streaming services on the concentration of digital music consumption[J]. Information Technology & People, 2019, 33(1): 160-179.
4 马闻锴, 李贵, 李征宇,等. 一种基于标签的Top-N个性化推荐算法[J]. 计算机科学, 2019, 46(): 224-229.
Ma Wen-kai, Li Gui, Li Zheng-yu, et al. A Top-N personalized recommendation algorithm based on tags[J]. Computer Science, 2019, 46(Sup.2): 224-229.
5 张鹏飞, 王宜贵, 张志军. 融合标签和多元信息的个性化推荐算法研究[J]. 计算机工程与应用, 2019, 55(5): 159-165.
Zhang Peng-fei, Wang Yi-gui, Zhang Zhi-jun. Research on personalized recommendation algorithm combining tags and multiple information[J]. Computer Engineering and Applications, 2019, 55(5): 159-165.
6 Davis R F, Baese-Berk M M, Stilp C. Effects of pitch contour and speaking rate on perception of foreign-accented speech[J]. The Journal of the Acoustical Society of America, 2019, 145(3): 1827.
7 Dewan S, Ho Y J, Ramaprasad J. Popularity or proximity: characterizing the nature of social influence in an online music community[J]. Operations Research, 2018, 58(3): 267-270.
8 Jain B J, Schultz D. Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces[J]. Pattern Recognition, 2018, 76: 349-366.
9 李宾, 周旭, 梅芳, 等. 基于K-means和矩阵分解的位置推荐算法[J]. 吉林大学学报: 工学版, 2019, 49(5): 1653-1660.
Li Bin, Zhou Xu, Mei Fang, et al. Location recommendation algorithm based on K-means and matrix factorization[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5):1653-1660.
10 Wang X. Research on the improved method of fundamental frequency extraction for music automatic recognition of piano music[J]. Journal of Intelligent and Fuzzy Systems, 2018, 35(3): 1-7.
11 Dazhi X U. Research on music culture personalized recommendation based on factor decomposition machine[J]. Personal and Ubiquitous Computing, 2020, 24(2): 247-257.
12 刘洲洲, 尹文晓, 张倩昀, 等. 基于离散优化算法和机器学习的传感云入侵检测[J]. 吉林大学学报: 工学版, 2020, 50(2): 692-702.
Liu Zhou-zhou, Yin Wen-xiao, Zhang Qian-yun, et al. Sensor cloud intrusion detection based on discrete optimization algorithm and machine learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 692-702.
13 张璠, 李为相, 李为. 基于从众心理矩阵和加权相似度的推荐算法[J]. 计算机工程与设计, 2019, 40(6): 1616-1620.
Zhang Pan, Li Wei-xiang, Li Wei. Recommendation algorithm based on herd mental matrix and weighted similarity[J]. Computer Engineering and Design, 2019, 40(6): 1616-1620.
14 王瑶, 寇月, 申德荣, 等. 元路径选择和矩阵分解的跨社交网络链路预测[J]. 计算机科学与探索, 2019, 13(9): 1459-1470.
Wang Yao, Kou Yue, Shen De-rong, et al. Cross-social network link prediction based on meta-path selection and matrix decomposition[J]. Journal of Computer Science and Exploration, 2019, 13(9): 1459-1470.
15 周本金, 陶以政, 纪斌, 等. 最小化误差平方和k-means初始聚类中心优化方法[J]. 计算机工程与应用, 2018, 54(15): 48-52.
Zhou Ben-jin, Tao Yi-zheng, Ji Bin, et al. K-means initial clustering center optimization method to minimize the error square sum[J]. Computer Engineering and Applications, 2018, 54(15): 48-52.
16 张铁赢, 黄贵, 章颖强, 等. X-DB:软硬一体的新型数据库系统[J]. 计算机研究与发展, 2018, 55(2): 319-326.
Zhang Tie-ying, Huang Gui, Zhang Ying-qiang, et al. X-DB: a new type of database system integrating software and hardware[J]. Journal of Computer Research and Development, 2018, 55(2): 319-326.
17 徐毅, 叶卫根, 戴鑫, 等. 融合用户信任度与相似度的推荐算法研究[J]. 小型微型计算机系统, 2018, 39(1): 78-83.
Xu Yi, Ye Wei-gen, Dai Xin, et al. Research on recommendation algorithm combining user trust and similarity[J]. Small Microcomputer System, 2018, 39(1): 78-83.
18 Xia J, Li G, Cao Z, et al. Personalized exercise recommendation algorithm combining learning objective and assignment feedback[J]. Journal of Intelligent & Fuzzy Systems, 2018, 35(3): 2965-2973.
19 Cao B, Chen J, Liu J, et al. A topic attention mechanism and factorization machines based mobile application recommendation method[J]. Mobile Networks and Applications, 2020(4): 1-12.
20 赖奕安, 张玉洁, 杜雨露, 等. 一种基于协同上下文关系学习的同城活动推荐算法[J]. 软件学报, 2020, 31(2): 421-438.
Lai Yi-an, Zhang Yu-jie, Du Yu-lu, et al. A recommendation algorithm for intra-city activities based on collaborative context learning[J]. Journal of Software, 2020, 31(2): 421-438.
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