Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis
This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to se...
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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/10.1155/2021/5579086 |
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| _version_ | 1832568483723870208 |
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| author | Juanjuan Shi |
| author_facet | Juanjuan Shi |
| author_sort | Juanjuan Shi |
| collection | DOAJ |
| description | This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory (LSTM) technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified. |
| format | Article |
| id | doaj-art-2d7a1c54bd2e4d70833b3cc7f7937ec9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2d7a1c54bd2e4d70833b3cc7f7937ec92025-02-03T00:58:59ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55790865579086Music Recommendation Algorithm Based on Multidimensional Time-Series Model AnalysisJuanjuan Shi0ZhongShan Polytechnic, Zhongshan, Guangdong 528400, ChinaThis paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory (LSTM) technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified.http://dx.doi.org/10.1155/2021/5579086 |
| spellingShingle | Juanjuan Shi Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis Complexity |
| title | Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis |
| title_full | Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis |
| title_fullStr | Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis |
| title_full_unstemmed | Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis |
| title_short | Music Recommendation Algorithm Based on Multidimensional Time-Series Model Analysis |
| title_sort | music recommendation algorithm based on multidimensional time series model analysis |
| url | http://dx.doi.org/10.1155/2021/5579086 |
| work_keys_str_mv | AT juanjuanshi musicrecommendationalgorithmbasedonmultidimensionaltimeseriesmodelanalysis |