Collaborative Score Prediction Method for Non-Random Missing Data

被引：0
|
作者：
Gu W. [1 ]
Xie X. [2 ]
Zhang Z. [3 ]
Mao Y. [1 ]
Liang Z. [1 ]
He Y. [1 ]
机构：
[1] School of Mathematics and Information, South China Agricultural University, Guangzhou
[2] School of Economics, Jinan University, Guangzhou
[3] School of Mathematics, South China University of Technology, Guangzhou
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2021年 / 49卷 / 01期
基金：
国家重点研发计划;
关键词：
Matrix decomposition; Recommendation system; Score prediction; Singular value decomposition;
D O I：
10.12141/j.issn.1000-565X.200210
中图分类号：
学科分类号：
摘要：
Most score prediction studies are based on the assumption that the missing values are random. However, the missing data of the score matrix of the actual on-line recommendation system is non-random. Incorrect assumptions about the missing data can lead to biased parameter estimation and prediction. In order to improve the accuracy of non-random missing score matrix filling, the internal principle of user and item score matrix was analyzed in this paper. It presents a method to transform the score matrix of user and object into the equivalent bilateral block dia-gonal matrix by row or column transformation. Then the matrix decomposition method was applied to different blocks to decompose and predict the score, making local data update and decomposition become a reality. The experimental results on the public test dataset show that the proposed method can improve the score filling effect, solve the problem of non-random score missing effectively, and improve the prediction accuracy of the recommendation system. The improved block matrix also has a better speedup ratio in the distributed processing experiment, which shows that the proposed method has better scalability. © 2021, Editorial Department, Journal of South China University of Technology. All right reserved.
引用
收藏
页码：47 / 57
页数：10
相关论文
共 28 条
  • [1] BEEL J, GIPP B, LANGER S, Et al., Paper recommender systems: a literature survey, International Journal on Digital Libraries, 17, 4, pp. 305-338, (2016)
  • [2] WEST J D, SMITH W I, BERGSTROM C T., A recommendation system based on hierarchical clustering of an article-level citation network, IEEE Transactions on Big Data, 2, 2, pp. 113-123, (2016)
  • [3] LIU X, SONG Y, AGGARWAL C, Et al., BiCycle: item recommendation with life cycles, Proceedings of 2017 IEEE International Conference on Data Mining, pp. 297-306, (2017)
  • [4] NIKOLAKOPOULOS A N, KOUNELI M A, GAROFA-LAKIS J D., Hierarchical itemspace rank: exploiting hierarchy to alleviate sparsity in ranking-based recommendation, Neurocomputing, 163, pp. 126-136, (2015)
  • [5] YAO L, SHENG Q Z, NGU A H H, Et al., Things of interest recommendation by leveraging heterogeneous relations in the Internet of things, ACM Transactions on Internet Technology, 16, 2, pp. 1-25, (2016)
  • [6] WANG J, SUN J, LIN H, Et al., Convolutional neural networks for expert recommendation in community question answering, Science China Information Sciences, 60, 11, (2017)
  • [7] LOPS P, JANNACH D, MUSTO C, Et al., Trends in content-based recommendation, User Modeling and User-Adapted Interaction, 29, 2, pp. 239-249, (2019)
  • [8] WEI J, HE J, CHEN K, Et al., Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications, 69, pp. 29-39, (2017)
  • [9] YANG Z, WU B, ZHENG K, Et al., A survey of collaborative filtering-based recommender systems for mobile Internet applications, IEEE Access, 4, pp. 3273-3287, (2016)
  • [10] ALMUTAIRI F M, SIDIROPOULOS N D, KARYPIS G., Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization, IEEE Journal of Selected Topics in Signal Processing, 11, 5, pp. 729-741, (2017)
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