Music Personalized Label Clustering and Recommendation Visualization
With the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity p...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5513355 |
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| _version_ | 1832566396199895040 |
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| author | Yongkang Huo |
| author_facet | Yongkang Huo |
| author_sort | Yongkang Huo |
| collection | DOAJ |
| description | With the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity problems. First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. Based on these algorithms and based on the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed tags, which can provide basic music services to users and push them personalized music recommendation lists. Then, the collaborative filtering algorithm based on tags is introduced. Usually this method uses discrete tags, and the user tags and music tags are juxtaposed and leveled with each other, which does not reflect the importance and ranking order relationship of each tag and does not reflect the cognitive sequence of users when they listen to and annotate music. In order to improve this problem and increase the accuracy of recommendations, the user-tag and music-tag data are correlated through the tag sequence of tag and music-tag data are correlated and modeled analytically, and feature directed graphs are constructed. |
| format | Article |
| id | doaj-art-372bd58d36204f7eb77848b1c57b30c1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-372bd58d36204f7eb77848b1c57b30c12025-02-03T01:04:14ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55133555513355Music Personalized Label Clustering and Recommendation VisualizationYongkang Huo0Conservatory of Music, Shandong University of Arts, Jinan 250014, Shandong, ChinaWith the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity problems. First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. Based on these algorithms and based on the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed tags, which can provide basic music services to users and push them personalized music recommendation lists. Then, the collaborative filtering algorithm based on tags is introduced. Usually this method uses discrete tags, and the user tags and music tags are juxtaposed and leveled with each other, which does not reflect the importance and ranking order relationship of each tag and does not reflect the cognitive sequence of users when they listen to and annotate music. In order to improve this problem and increase the accuracy of recommendations, the user-tag and music-tag data are correlated through the tag sequence of tag and music-tag data are correlated and modeled analytically, and feature directed graphs are constructed.http://dx.doi.org/10.1155/2021/5513355 |
| spellingShingle | Yongkang Huo Music Personalized Label Clustering and Recommendation Visualization Complexity |
| title | Music Personalized Label Clustering and Recommendation Visualization |
| title_full | Music Personalized Label Clustering and Recommendation Visualization |
| title_fullStr | Music Personalized Label Clustering and Recommendation Visualization |
| title_full_unstemmed | Music Personalized Label Clustering and Recommendation Visualization |
| title_short | Music Personalized Label Clustering and Recommendation Visualization |
| title_sort | music personalized label clustering and recommendation visualization |
| url | http://dx.doi.org/10.1155/2021/5513355 |
| work_keys_str_mv | AT yongkanghuo musicpersonalizedlabelclusteringandrecommendationvisualization |