大数据在音乐推荐质量提升中的实践及应用

基于音乐推荐的应用实例,探索和实现了大数据如何提高推荐质量的过程及方法。提出通过建立基于RFM模型的用户歌曲综合评分体系,在推荐算法中引入项目稀疏度、重叠度、可信度概念作为调整因子,在混合推荐时引入飙升词、内容标签和二次规则过滤等组合方法以解决推荐系统面临的常见问题,为大数据应用提供具体的参考和指导。...

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Bibliographic Details
Published in	电信科学 Vol. 30; no. 10; pp. 43 - 47
Main Author	张玉忠方艾金铎袁立宇
Format	Journal Article
Language	Chinese
Published	中国通信学会 01.10.2014 人民邮电出版社有限公司中国电信股份有限公司广东研究院广州510630
Subjects	RFM模型协同过滤推荐大数据协同过滤推荐 RFM模型大数据 big data RFM model collaborative filtering recommendation
Online Access	Get full text
ISSN	1000-0801
DOI	10.3969/j.issn.1000-0801.2014.10.008

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Summary:	基于音乐推荐的应用实例,探索和实现了大数据如何提高推荐质量的过程及方法。提出通过建立基于RFM模型的用户歌曲综合评分体系,在推荐算法中引入项目稀疏度、重叠度、可信度概念作为调整因子,在混合推荐时引入飙升词、内容标签和二次规则过滤等组合方法以解决推荐系统面临的常见问题,为大数据应用提供具体的参考和指导。
Bibliography:	The process and methods of big data improved recommendation quality in music applications werefocused. The individual music score system referring RFM model was described. The sparsity and overlapping andreliability was applied to adjust recommendation algorithm, combined soaring words and content labels and filteringrules with mixing recommendation to resolve the common problems of recommendation system. 11-2103/TN Zhang Yuzhong, Fang Ai, Jin Duo, Yuan Liyu （Guangdong Research Institute of China Telecom Co., Ltd., Guangzhou 510630, China） big data, RFM model, collaborative filtering recommendation
ISSN:	1000-0801
DOI:	10.3969/j.issn.1000-0801.2014.10.008