一种改进的均方差协同过滤算法

doi:10.3969/j.issn.1006-2475.2019.04.002

摘要/Abstract

摘要： 由于传统基于均方差的协同过滤算法(MSD)计算相似性时仅考虑评分向量间均方差值，导致其推荐性能不理想，针对这个问题，提出融合评分向量间余弦值和均方差值的改进均方差协同过滤算法(Improved MSD， IMSD)。通过在2个Movielens数据集上进行实验表明，IMSD算法较MSD算法的推荐准确度有所提高。更为重要的是，将IMSD算法进行推广应用，也能够取得较好的效果。本文将其应用于改进另外2种算法,即JAC_MSD和AC_MSD算法,并提出了2种相应的JAC_IMSD和AC_IMSD算法，发现算法的推荐准确度都有所提高。在所研究的几种算法中，AC_IMSD算法推荐准确度最优。

关键词: 推荐系统, 协同过滤, MSD算法

Abstract: Traditional collaborative filtering algorithm based on the mean square difference (MSD) only considers the mean square difference value between the rating vectors when calculating the similarity, resulting in an unsatisfactory recommendation performance. To solve this problem, we propose an improved mean square difference collaborative filtering algorithm (IMSD), which integrates the cosine value and the mean square difference value between the rating vectors. Experiments on two Movielens datasets show that the IMSD algorithm improves the recommendation accuracy compared with the MSD algorithm. More importantly, we find that its generalized application is also effective. By applying the IMSD into improving two other algorithms, JAC_MSD and AC_MSD algorithms, we propose two corresponding JAC_IMSD and AC_IMSD algorithms, and find that the recommendation accuracy of both algorithms can be improved. Among all the investigated algorithms, the recommendation accuracy of the AC_IMSD algorithm is best.

Key words: recommender system, collaborative filtering, MSD algorithm

中图分类号:

TP391

饶钰，陈光，邱天. 一种改进的均方差协同过滤算法[J]. 计算机与现代化, 2019, 0(04): 6-.

RAO Yu, CHEN Guang, QIU Tian. An Improved Mean Square Difference Collaborative Filtering Algorithm[J]. Computer and Modernization, 2019, 0(04): 6-.

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