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音乐个性化推荐算法RR-UBPMF的研究
王猛,叶西宁
0
(华东理工大学信息科学与工程学院, 上海 200237)
摘要:
大规模隐式反馈数据的使用是推荐系统中的研究热点和难点问题。针对隐式反馈数据高噪声和缺少负反馈的特点,以音乐推荐为背景,在研究概率矩阵分解模型(PMF)的基础上提出了一种直接优化排名倒数(RR)的概率矩阵分解模型(RR-PMF)。通过与User-based KNN算法相结合提出了RR-UBPMF算法,并利用交叉最小二乘法(ALS)进行优化学习。在last.fm数据集上的实验结果表明,该算法在准确率(Precision)、尤其是在标准化折算累加值(NDCG)等评价指标上表现出极大的优势,能够明显提高预测准确性,并且具有良好的可拓展性。
关键词:  推荐系统  协同过滤  排名倒数  概率矩阵分解  KNN
DOI:10.14135/j.cnki.1006-3080.2017.01.018
投稿时间:2016-07-20
基金项目:国家自然科学基金(60974066)
RR-UBPMF, A Personalized Music Recommendation Algorithm
WANG Meng,YE Xi-ning
(School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China)
Abstract:
The application of massive implicit feedback data is one of hot and difficult issues in the research of recommendation system.Aiming at the high noise and less negative feedback of implicit feedback data,this paper proposes a model of RR-PMF based on probabilistic matrix factorization (PMF),which optimizes the ranked reciprocal (RR) directly.By combining with the user-based KNN,this paper proposes a RR-UBPMF method,which is optimized via alternative least squares (ALS).The experiment via the last.fm dataset shows that the proposed algorithm has great advantages in the evaluation index of precision and NDCG,and can significantly improve the prediction accuracy and has good scalability.
Key words:  recommended system  collaborative filtering  reciprocal rank  probabilistic matrix factorization  KNN

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