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, doi: 10.3969/j.issn.1006-2475.2019.04.002.

RAO Yu, CHEN Guang, QIU Tian. An Improved Mean Square Difference Collaborative Filtering Algorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.04.002.

参考文献［27］

［1］	李书宁. 互联网信息环境中信息超载问题研究［J］. 情报科学, 2005,23(10):1587-1590.
［2］	邹裕,肖倩,吴树荣. 基于增强关联规则挖掘的大型网站推荐系统［J］. 计算机与现代化, 2016(10):30-34.
［3］	BALABANOVI〖XCC.TIF,JZ〗 M, SHOHAM Y. Fab: Content-based, collaborative recommendation［J］. Communication of the ACM, 1997,40(3):66-72.
［4］	BOBADILLA J, ORTEGA F, HERNANDO A, et al. Recommender systems survey［J］. Knowledge-Based Systems, 2013,46:109-132.
［5］	AL-SHAMRI M Y H. User profiling approaches for demographic recommender systems［J］. Knowledge-Based Systems, 2016,100:175-187.
［6］	SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms［C］// International Conference on World Wide Web. ACM, 2001:285-295.
［7］	杨秀萍. 融合用户评分和属性相似度的协同过滤推荐算法［J］. 计算机与现代化, 2017(7):16-19.
［8］	LU Z Q, PAN S J, LI Y, et al. Collaborative evolution for user profiling in recommender systems［C］// Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016:3804-3810.
［9］	CAAMARES R, CASTELLS P. A probabilistic reformulation of memory-based collaborative filtering: Implications on popularity biases［C］// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017:215-224.
［10］	SARWAR B, KARYPIS G, KONSTAN J, et al. Application of Dimensionality Reduction in Recommender System——A Case Study［R］. Minnesota Univ Minneapolis Dept of Computer Science, 2000.
［11］	SALAKHUTDINOV R R,MNIH A. Probabilistic matrix factorization［C］// Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007:1257-1264.
［12］	HERNANDO A, BOBADILLA J, ORTEGA F. A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model［J］. Knowledge-Based Systems, 2016,97:188-202.
［13］	KOREN Y. Collaborative filtering with temporal dynamics［J］. Communications of the ACM, 2010,53(4):89-97.
［14］	KOREN Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model［C］// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008:426-434.
［15］	ZAHRA S, GHAZANFAR M A, KHALID A, et al. Novel centroid selection approaches for KMeans-clustering based recommender systems［J］. Information Sciences, 2015,320:156-189.
［16］	ROH T H, OH K J, HAN I. The collaborative filtering recommendation based on SOM cluster-indexing CBR［J］. Expert Systems with Applications, 2003,25(3):413-423.
［17］	YAGER R R. Fuzzy logic methods in recommender systems［J］. Fuzzy Sets and Systems, 2003,136(2):133-149.
［18］	BIRTOLO C, RONCA D. Advances in clustering collaborative filtering by means of fuzzy C-means and trust［J］. Expert Systems with Applications, 2013,40(17):6997-7009.
［19］	SUN Z B, HAN L X, HUANG W L, et al. Recommender systems based on social networks［J］. Journal of Systems and Software, 2015,99(C):109-119.
［20］	MORADI P, AHMADIAN S, AKHLAGHIAN F. An effective trust-based recommendation method using a novel graph clustering algorithm［J］. Physica A: Statistical Mechanics and ITS Applications, 2015,436:462-481.
［21］	LIU X J. An improved clustering-based collaborative filtering recommendation algorithm［J］. Cluster Computing, 2017,20(2):1281-1288.
［22］	王艳,王移芝. 融合信任关系的协同过滤推荐算法改进研究［J］. 计算机与现代化, 2016(12):7-11.
［23］	BOBADILLA J, SERRADILLA F, BERNAL J. A new collaborative filtering metric that improves the behavior of recommender systems［J］. Knowledge-Based Systems, 2010,23(6):520-528.
［24］	PIRASTEH P, HWANG D, JUNG J E. Weighted similarity schemes for high scalability in user-based collaborative filtering［J］. Mobile Networks & Applications, 2015,20(4):497-507.
［25］	LIU H F, HU Z, MIAN A, et al. A new user similarity model to improve the accuracy of collaborative filtering［J］. Knowledge-Based Systems, 2014,56:156-166.
［26］	HASAN M, AHMED S, MALIK M A I, et al. A comprehensive approach towards user-based collaborative filtering recommender system［C］// IEEE International Workshop onComputational Intelligence (IWCI). 2016:159-164.
［27］	KIM S, KIM H, MIN J K. An efficient parallel similarity matrix construction on MapReduce for collaborative filtering［J］. The Journal of Supercomputing, 2019,75(1):123-141.

[1]	刘国丽, 徐洪楠, 谭有倩. 结合专家信任的协同过滤推荐算法研究[J]. 计算机与现代化, 2022, 0(11): 60-68.
[2]	张宗海, 於跃成, 冯申. 融合三重注意力和评论评分的深度推荐算法[J]. 计算机与现代化, 2022, 0(05): 1-9.
[3]	文勇军, 何环晶, 唐立军, . 基于LDA的隐式标签协同过滤推荐算法[J]. 计算机与现代化, 2022, 0(03): 53-58.
[4]	刘亦欣, 王家伟, 李自力. 融合注意力与深度因子分解机的时间上下文推荐模型[J]. 计算机与现代化, 2021, 0(11): 22-27.
[5]	陈卓, 袁玺明, 杜军威. 在线问答社区——海川化工论坛的回答者推荐算法[J]. 计算机与现代化, 2021, 0(10): 29-34.
[6]	耿化聪, 梁宏涛, 刘国柱. 基于知识图谱与协同过滤的饮食推荐算法[J]. 计算机与现代化, 2021, 0(08): 24-29.
[7]	王杉文, 欧鸥, 马万民, 陈建林. 基于用户信息向量聚类和改进SAMME的推荐算法[J]. 计算机与现代化, 2021, 0(07): 23-28.
[8]	黄皓, 陈荔. 基于信息熵和改进相似度协同过滤算法[J]. 计算机与现代化, 2021, 0(06): 29-34.
[9]	石朋, 姚文明, 王祥. 融合用户模糊聚类和相似度的加权Slope One优化[J]. 计算机与现代化, 2021, 0(01): 70-75.
[10]	崔鑫. 基于异构信息网络的推荐系统[J]. 计算机与现代化, 2020, 0(12): 13-19.
[11]	陈卓, 李鑫, 杜军威, 袁玺明. 面向化工企业事故的根原因关联分析[J]. 计算机与现代化, 2020, 0(10): 1-6.
[12]	徐建鹏, 徐祥, 王晖, 伍琼, 王杰. 基于用户行为和新闻时效性的农业信息协同过滤推荐算法[J]. 计算机与现代化, 2020, 0(07): 6-10.
[13]	李一野1，2，邓浩江1，2. 基于改进余弦相似度的协同过滤推荐算法[J]. 计算机与现代化, 2020, 0(01): 69-.
[14]	姜书浩1，2，张立毅1，2，周娜1. 基于用户偏好动态变化的协同过滤推荐[J]. 计算机与现代化, 2020, 0(01): 75-.
[15]	郑策1,2,尤佳莉1,2. 电影推荐系统中基于图的协同过滤算法[J]. 计算机与现代化, 2019, 0(11): 38-.