Personalized Music Recommendation Based on Interest and Emotion: A Comparison of Multiple Algorithms

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Xiuli Yan
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引用次数: 0

Abstract

Recommendation algorithms can greatly improve the efficiency of information retrieval for users. This article briefly introduced recommendation algorithms based on association rules and algorithms based on interest and emotion analysis. After crawling music and comment data from the NetEase Cloud platform, a simulation experiment was conducted. Firstly, the performance of the Back-Propagation Neural Network (BPNN) in the interest and emotion-based algorithm for recommending music was tested, and then the impact of the proportion of emotion weight between comments and music on the emotion analysis-based algorithm was tested. Finally, the three recommendation algorithms based on association rules, user ratings, and interest and emotion analysis were compared. The results showed that when the BPNN used the dominant interest and emotion and secondary interest and emotion as judgment criteria, the accuracy of interest and emotion recognition for music and comments was higher. When the proportion of interest and emotion weight between comments and music was 6:4, the interest and emotion analysis-based recommendation algorithm had the highest accuracy. The interest and emotion-based recommendation algorithm had higher recommendation accuracy than the association rule-based and user rating-based algorithms, and could provide users with more personalized and emotional music recommendations. Keywords—Interest and emotion; recommendation algorithm; music; personalization
基于兴趣和情感的个性化音乐推荐:多种算法的比较
推荐算法可以极大地提高用户信息检索的效率。本文简要介绍了基于关联规则的推荐算法和基于兴趣和情感分析的推荐算法。从网易云平台抓取音乐和评论数据,进行仿真实验。首先测试了反向传播神经网络(BPNN)在基于兴趣和情感的音乐推荐算法中的性能,然后测试了评论和音乐之间的情感权重比例对基于情感分析的算法的影响。最后,对基于关联规则、用户评分、兴趣和情感分析的三种推荐算法进行了比较。结果表明,当BPNN以主导兴趣和情感和次要兴趣和情感作为判断标准时,对音乐和评论的兴趣和情感识别准确率更高。当评论与音乐的兴趣和情感权重比例为6:4时,基于兴趣和情感分析的推荐算法准确率最高。基于兴趣和情感的推荐算法比基于关联规则和基于用户评分的推荐算法具有更高的推荐准确率,可以为用户提供更加个性化和情感化的音乐推荐。关键词:兴趣与情感;推荐算法;音乐;个性化
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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