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

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.