Recommendation Algorithm Based on 4th-order Singular Value Decomposition

被引：0
|
作者：
Guo Q. [1 ]
Yue Q. [1 ]
Li R.-D. [1 ]
Liu J.-G. [2 ]
机构：
[1] Complex Systems Science Research Center, University of Shanghai for Science and Technology, Yangpu, Shanghai
[2] Institute of Accounting and Finance, Shanghai University of Finance and Economics, Yangpu, Shanghai
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2019年 / 48卷 / 04期
关键词：
4-th order; Multidimensional information; Recommendation algorithm; Singular value decomposition;
D O I：
10.3969/j.issn.1001-0548.2019.04.017
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
The 3rd-order singular value decomposition recommendation algorithm can comprehensively consider the three parts of information of user, tag and item, and explore the potential relationship between the three to make recommendations. However, this method does not introduce any other effective information, such as the user's emotion. Considering more dimension information, in this paper we propose a 4th-order singular value decomposition recommendation algorithm based on the third-order one. The method extracts user's emotional preference from the emoji expression in the commentary, introduces a 4th-order tensor model to store user, user emotion, tag, and item quad data, and applies the 4th-order singular value decomposition to make personalized recommendations. The experimental results on an empirical dataset of online internet education shows that the proposed method has a significant improvement in accuracy and recall performance than the third-order singular value decomposition recommendation algorithm and the traditional recommendation algorithms. In the Top-1 recommendation, the accuracy rate and recall rate of proposed method can reach 0.513 and 0.339. The work of this paper provides a reference for personalized recommendation of mobile. © 2019, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.
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收藏
页码：586 / 594
页数：8
相关论文
共 29 条
  • [1] Zhang L., Bai L.-S., Zhou T., Crossing recommendation based on multi-B2C behavior, Journal of University of Electronic Science & Technology of China, 42, 1, pp. 154-160, (2013)
  • [2] Zhang H.-X., Lu Z., Zhang C.-T., Et al., An improved collaborative filtering recommendation algorithm with weighted heterogeneous information, Journal of University of Electronic Science & Technology of China, 47, 1, pp. 112-116, (2018)
  • [3] Ding Z., Qin Z., Zheng W.-T., Et al., A recommendation model based on browsing behaviors of mobile users, Journal of University of Electronic Science & Technology of China, 46, 6, pp. 907-912, (2017)
  • [4] Liu J.-G., Zhou T., Guo Q., Et al., Overview of the evaluated algorithms for the personal recommendation systems, Complex Systems & Complexity Science, 6, 3, pp. 1-10, (2009)
  • [5] Liu G.-S., Xie X.-R., Huang H.-B., Et al., Fast computing method for items recommendation based on shortest-path trust relationship, Journal of University of Electronic Science & Technology of China, 43, 2, pp. 162-166, (2014)
  • [6] Zhou T., Ren J., Medo M., Et al., Bipartite network projection and personal recommendation, Physical Review E, 76, 4, (2007)
  • [7] Sarwar B.M., Karypis G., Konstan J.A., Et al., Application of dimensionality reduction in recommender system-a case study, ACM Web kdd Workshop, (2000)
  • [8] Heymann P., Koutrika G., Garcia-Molina H., Can social bookmarking improve web search?, Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 195-206, (2008)
  • [9] Symeonidis P., Nanopoulos A., Manolopoulos Y., Tag recommendations based on tensor dimensionality reduction, Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 43-50, (2008)
  • [10] Symeonidis P., Clusthosvd: Item recommendation by combining semantically enhanced tag clustering with tensor HOSVD, IEEE Transactions on Systems Man & Cybernetics Systems, 46, 9, pp. 1240-1251, (2016)
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