计算机应用研究2018，Vol.35Issue(4)：1010-1013,4.DOI:10.3969/j.issn.1001-3695.2018.04.011
面向音乐推荐的全变差图非负矩阵分解方法
Facing music recommended total variation non-negative matrix decomposition method
摘要
Abstract
The current music recommendation system is based on the characteristics of the song and the contextual factors.However,there are many interference factors in the feature selection,which makes the noise jamming.To solve this problem,this paper proposed a method of total variation non-negative matrix factorization for music recommendation,by taking into account the influence of many factors and by means of total variation to reduce noise error.In order to improve the accuracy of prediction,the objective of the method was to optimize the loss function.The real data set experiments show that significantly improve the prediction accuracy,especially for fuzzy types of songs can also have a better recommendation effect,better meet the personalized needs of mobile music service.关键词
推荐系统/非负矩阵分解/全变差/音频特征Key words
recommender system/non-negative matrix factorization/total variation (TV)/audio features分类
信息技术与安全科学引用本文复制引用
滕少华,郑明,刘冬宁..面向音乐推荐的全变差图非负矩阵分解方法[J].计算机应用研究,2018,35(4):1010-1013,4.基金项目
国家自然科学基金资助项目(61402118) （61402118）
广东省科技计划资助项目(2013B090200017,2013B010401029,2013B010401034,2015B090901016) （2013B090200017,2013B010401029,2013B010401034,2015B090901016）
