{"title":"基于传感器和信息检索技术的个性化音乐教学服务推荐","authors":"Hui Lu","doi":"10.1016/j.measen.2024.101207","DOIUrl":null,"url":null,"abstract":"<div><p>In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.</p></div>","PeriodicalId":34311,"journal":{"name":"Measurement Sensors","volume":"33 ","pages":"Article 101207"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665917424001831/pdfft?md5=c146f8d2d70ef910061848fdfa0d2269&pid=1-s2.0-S2665917424001831-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Personalized music teaching service recommendation based on sensor and information retrieval technology\",\"authors\":\"Hui Lu\",\"doi\":\"10.1016/j.measen.2024.101207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.</p></div>\",\"PeriodicalId\":34311,\"journal\":{\"name\":\"Measurement Sensors\",\"volume\":\"33 \",\"pages\":\"Article 101207\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2665917424001831/pdfft?md5=c146f8d2d70ef910061848fdfa0d2269&pid=1-s2.0-S2665917424001831-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2665917424001831\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement Sensors","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2665917424001831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Personalized music teaching service recommendation based on sensor and information retrieval technology
In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.