计算机工程与应用2019，Vol.55Issue(2)：116-120,5.DOI:10.3778/j.issn.1002-8331.1709-0347
基于LFM矩阵分解的推荐算法优化研究
Research on Improved Recommendation Algorithm Based on LFM Matrix Factorization
摘要
Abstract
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 out-comes. 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.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Key words
matrix factorization/Latent Factor Model(LFM)/recommendation algorithm/batch learning algorithm with momentum/mixed learning algorithm分类
信息技术与安全科学引用本文复制引用
陈晔,刘志强..基于LFM矩阵分解的推荐算法优化研究[J].计算机工程与应用,2019,55(2):116-120,5.基金项目
国家高技术研究发展计划(863)(No.SS2013AA041003). （863）
