摘要
在推荐系统中,基于矩阵分解的推荐算法是目前的研究热点之一,然而普通矩阵分解算法的推荐精确度偏低,为了改善该问题,以矩阵分解算法中的潜在因子模型(LFM)优化为研究对象,分析LFM中两种基础推荐算法在寻优速率与推荐精度上的不足,然后提出两种改进算法:带冲量的批量学习算法和混合学习算法,最后通过实验数据测试,对比了不同算法的推荐效果,结果证明改进算法的性能更优。
In the field of recommendation system, the Recommendation Algorithms(RA)based on matrix factorization is one of research hotspots. To improve the problem, this paper focuses on the algorithm improvement of Latent Factor Model(LFM)in the matrix factorization based RA algorithms. Two basic algorithms are modified to provide more accurate outcomes. Finally, a numerical example, which is used to carry out comparative study among different algorithms, proves that the improved algorithm is better than previous works.
作者
陈晔
刘志强
CHEN Ye;LIU Zhiqiang(College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《计算机工程与应用》
CSCD
北大核心
2019年第2期116-120,167,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.71471087)
关键词
矩阵分解
潜在因子模型
推荐算法
带冲量的批量学习算法
混合学习算法
matrix factorization
Latent Factor Model(LFM)
recommendation algorithm
batch learning algorithm with momentum
mixed learning algorithm