An Improved Mean Square Difference Collaborative Filtering Algorithm

doi:10.3969/j.issn.1006-2475.2019.04.002

Abstract

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

CLC Number:

TP391

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.

References ［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]	LIU Guo-li, XU Hong-nan, TAN You-qian. Collaborative Filtering Recommendation Algorithm Combined with Expert Trust [J]. Computer and Modernization, 2022, 0(11): 60-68.
[2]	WEN Yong-jun, HE Huan-jing, TANG Li-jun, . Implicit Tag Collaborative Filtering Recommendation Algorithm Based on LDA [J]. Computer and Modernization, 2022, 0(03): 53-58.
[3]	GENG Hua-cong, LIANG Hong-tao, LIU Guo-zhu. Recipe Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering [J]. Computer and Modernization, 2021, 0(08): 24-29.
[4]	HUANG Hao, CHEN Li. A Collaborative Filtering Algorithm Based on Information Entropy and Improved Similarity [J]. Computer and Modernization, 2021, 0(06): 29-34.
[5]	SHI Peng, YAO Wen-ming, WANG Xiang. Weighted Slope One Optimization Combining User Fuzzy Clustering and Similarity [J]. Computer and Modernization, 2021, 0(01): 70-75.
[6]	CUI Xin. Recommendation System Based on Heterogeneous Information Network [J]. Computer and Modernization, 2020, 0(12): 13-19.
[7]	CHEN Zhuo, LI Xin, DU Jun-wei, YUAN Xi-ming. Root Cause Correlation Analysis of Chemical Enterprise Accident [J]. Computer and Modernization, 2020, 0(10): 1-6.
[8]	XU Jian-peng, XU Xiang, WANG Hui, WU Qiong, WANG Jie. Agricultural Information Collaborative Filtering Recommendation Algorithm Based on User Behavior and News Timeliness [J]. Computer and Modernization, 2020, 0(07): 6-10.
[9]	LI Yi-ye 1,2, DENG Hao-jiang1,2. A Collaborative Filtering Recommendation Algorithm Based on Adjusted Cosine Similarity [J]. Computer and Modernization, 2020, 0(01): 69-.
[10]	JIANG Shu-hao1,2, ZHANG Li-yi1,2, ZHOU Na1. Collaborative Filtering Recommendation Based on Dynamic Changes of User Preferences [J]. Computer and Modernization, 2020, 0(01): 75-.
[11]	ZHENG Ce1,2, YOU Jia-li1,2. A Graph-based Collaborative Filtering Algorithm in Movie Recommendation System [J]. Computer and Modernization, 2019, 0(11): 38-.
[12]	XIE Hao-ran1, WEI Wei2, YANG Zhi-hui3, DENG Ju-zhi1, GE Kun-peng1. New Radio and Television Program Collaborative Recommendation Process Based on TF-IDF [J]. Computer and Modernization, 2019, 0(09): 65-.
[13]	CHEN Zhi-yu1,2,3,LYU Tan-yue2,WANG Fei2,DUAN Zhen-wei2 . Image Annotation System Based on Crowdsourcing [J]. Computer and Modernization, 2019, 0(08): 112-.
[14]	LIU Chun-xia， LU Jian-bo， WU Ling-mei. A Movie Recommendation Model: Solving Cold Start Problem [J]. Computer and Modernization, 2018, 0(11): 83-.
[15]	WU Ling-mei, LU Jian-bo, LIU Chun-xia. A Recommendation Algorithm Based on Denoising Autoencoders [J]. Computer and Modernization, 2018, 0(03): 78-.