Improving the accuracy of m-distance based nearest neighbor recommendation system by using ratings variance

被引：3

作者：

Hasanzadeh N. ^{[1
]}

Forghani Y. ^{[1
]}

机构：

[1] Islamic Azad University, Mashhad Branch, Mashhad

来源：

Ingenierie des Systemes d'Information | 2019年 / 24卷 / 02期

关键词：

Collaborative filtering; M-distance; MBR; Nearestneighbor; Recommendation system;

D O I：

10.18280/isi.240201

中图分类号：

学科分类号：

摘要：

M-distance based recommendation system (MBR) is a nearest neighbor basedrecommendation method which uses the average of ratings given to an item as the attribute ofthat item. This attribute is used to determine similar items. Then, the average of the ratinggiven to the similar items to an item of the active user determines the rating of that item. Inthis paper, to decrease the error of MBR, by combining the following ideas, eight MBR-basedrecommendation systems are proposed: (a) Using the variance of item ratings in addition tothe average of item ratings, as two attributes of an item, for determining similar items in anitem-based nearest neighbor method; (b) Using the variance of user ratings in addition to theaverage of user ratings, as two attributes of a user, for determining similar users in a user-basednearest neighbor method; (c) Using a weighted average method for combining the ratings ofsimilar items or similar users; (d) Using ensemble learning. Experimental results on realdatasets show that our proposed EVMBR and EWVMBR which use ensemble learning havethe least error. The error of the suggested EWVMBR is at-least 20% lower than that of MBR,Slope-One, P-kNN, and C-kNN. © 2019 International Information and Engineering Technology Association. All rights reserved.

引用

页码：131 / 137

页数：6

共 18 条

[11]

Yao Y., Three-way decisions with probabilistic rough sets, Information Sciences, 180, 3, pp. 341-353, (2010)

[12]

Yao Y., Rough sets and three-way decisions, International Conference on Rough Sets and Knowledge Technology, pp. 62-73, (2015)

[13]

Qi J., Qian T., Wei L., The connections between three-way and classical concept lattices, Knowledge-Based Systems, 91, 1, pp. 143-151, (2016)

[14]

Condli M.K., Lewis D.D., Madigan D., Posse C., Bayesian Mixed-E ects Models for Recommender Systems, ACM SIGIR, 99, (1999)

[15]

Yuan Y., Luo X., Shang M.S., Effects of preprocessing and training biases in latent factor models for recommender systems, Neurocomputing, 275, 1, pp. 2019-2030, (2018)

[16]

Ren L., Wang W., An SVM-based collaborative filtering approach for Top-N web services recommendation, Future Generation Computer Systems, 78, 1, pp. 531-543, (2018)

[17]

Vapnik V., The Nature of Statisticsal Learning, (1995)

[18]

Wu H., Zhang Z., Yue K., Zhang B., He J., Sun L., Dual-regularized matrix factorization with deep neural networks for recommender systems, Knowledge-Based Systems, 145, 1, pp. 46-58, (2018)

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