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Home > Archive>Volume 44, Issue 1, 2021 >78-87. DOI:10.11835/j.issn.1000-582X.2021.01.009
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Personalized recommendation system based on matrix factorization and XGBoost algorithm
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    Abstract:

    In order to solve the data sparsity problem and improve the recommendation accuracy, we proposed a new matrix factorization XGBoost(MFXGB) recommendation algorithm which combined the matrix factorization method and the XGBoost (Extreme Gradient Boosting) algorithm. SVD++ algorithm (SVD, singular value decomposition) was used to fill the user-item score matrix to lessen the influence on the accuracy of the algorithm due to too many missing values and XGBoost was used to build a supervised learning model to predict the user's score. To reduce the computation time, feature extration based on the K-means clustering method was proposed to train XGBoot. The proposed MFXGB algorithm was applied to MovieLens dataset for experimental analysis and the results show that the recommendation accuracy was improved by 8.91%,10.18% and 11.79% respectively, compared with the three traditional algorithms.

    Reference
    [1] Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12):61-70.
    [2] Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods[M]//Ricci F, Rokach L, Shapira B, Kantor P B. Recommender Systems Handbook. New York:Springer, 2011:107-144.
    [3] 韩亚楠, 曹菡, 刘亮亮. 基于评分矩阵填充与用户兴趣的协同过滤推荐算法[J]. 计算机工程, 2016, 42(01):36-40. Han Y N, Cao H, Liu L L. Collaborative filtering recommendation algorithm based on score matrix filling and user interest[J]. Computer Engineering, 2016, 42(01):36-40. (in Chinese)
    [4] Li J U, Chai Y H, Chen J R. Improved personalized recommendation based on user attributes clustering and score matrix filling[J]. Computer Standards & Interfaces, 2017, 57:59-67.
    [5] Guan X, Li C T, Guan Y. Matrix factorization with rating completion:an enhanced SVD model for collaborative filtering recommender systems[J]. IEEE Access, 2017, 5:27668-27678.
    [6] 王娟, 熊巍. 基于矩阵分解的最近邻推荐系统及其应用[J]. 统计与决策, 2019(6):17-20. Wang J, Xiong W. Collaborative filtering recommender system based on matrix factorization and its application[J]. Statistics & Decision, 2019(6):17-20.(in Chinese)
    [7] 石鸿瑗, 孙天昊, 李双庆, 等. 融合时间和类型特征加权的矩阵分解推荐算法[J]. 重庆大学学报, 2019, 42(1):79-87. Shi H Y, Sun T H, Li S Q, et al. A matrix factorization recommendation algorithm with time and type weight[J]. Journal of Chongqing University, 2019, 42(1):79-87.(in Chinese)
    [8] Bokde D, Girase S, Mukhopadhyay D. Matrix factorization model in collaborative filtering algorithms:a survey[J]. Procedia Computer Science, 2015, 49(1):136-146.
    [9] Wu Y, Liu X, Xie M, et al. CCCF:Improving collaborative filtering via scalable user-item co-clustering[C]//WSDM'16:Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2016:73-82.
    [10] Liao C L, Lee S J. A clustering based approach to improving the efficiency of collaborative filtering recommendation[J]. Electronic Commerce Research and Applications, 2016, 18:1-9.
    [11] Najafabadi M K, Mahrin M N, Chuprat S, et al. Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data[J]. Computers in Human Behavior, 2017, 67:113-128.
    [12] 胡思才, 孙界平, 琚生根, 等. 基于深度神经网络和概率矩阵分解的混合推荐算法[J].四川大学学报(自然科学版), 2019, 56(6):1033-1041. Hu S C, Sun J P, Ju S G, et al. Hybrid recommendation algorithm based on deep neural network and probabilistic matrix factorization[J]. Journal of Sichuan University(Natural Science Edition), 2019, 56(6):1033-1041. (in Chinese)
    [13] Wei J, He J H, Chen K, et al. Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert Systems With Applications, 2017, 69:29-39.
    [14] Wang X, He X, Feng F, et al. TEM:Tree-enhanced embedding model for explainable recommendation[C]//WWW'18:Proceedings of the 2018 World Wide Web Conference. 2018:1543-1552.
    [15] Chen T Q, Guestrin C. XGBoost:A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California USA. New York, NY, USA:ACM, 2016:785-794.
    [16] 张昊, 纪宏超, 张红宇. XGBoost算法在电子商务商品推荐中的应用[J]. 物联网技术, 2017(2):102-104. Zhang H, Ji H C, Zhang H Y. Application of the XGBoost algorithm in the e-commerce recommendation system[J]. Internet of Things Technologies, 2017(2):102-104. (in Chinese)
    [17] 齐德法, 徐连诚, 朱振方. 融合协同过滤的XGBoost推荐算法[J]. 计算机应用研究, 2020, 37(5):1317-1320. Qi D F, Xu L C, Zhu Z F. XGBoost recommendation algorithm with collaborative filtering[J]. Application Research of Computers, 2020, 37(5):1317-1320.(in Chinese)
    [18] Koren Y, Bell R. Advances in collaborative filtering[M]//Ricci F, Rokach L, Shapira B, Kantor P B. Recommender Systems Handbook. New York:Springer, 2011:145-186
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何婧,胡杰.融合矩阵分解和XGBoost的个性化推荐算法[J].重庆大学学报,2021,44(1):78~87

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  • Received:July 06,2020
  • Online: January 08,2021
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