基于自适应样本权重的矩阵分解推荐算法

摘要/Abstract

摘要： 稀疏数据矩阵缺失值估计是一项必要的基础性研究,在推荐系统中尤为重要,针对该问题的一种有效方法便是矩阵分解算法(Matrix Factorization,MF),但传统MF算法仅直接使用回归思想拟合矩阵样本点,并没有考虑样本自身拟合难易程度的差异性。针对该情况,文中分析提出了一种基于自适应样本权重的矩阵分解算法(AWS-MF),在原有MF算法的基础上,针对样本差异性进行有偏向模型拟合,为增加模型回归的准确性与稳定性,加权整合中间算法结果,从而得到最终的拟合数据值。实验结果表明,相比于MF算法和NMF算法,改进后的AWS-MF算法能根据样本差异性自动调整样本权重占比,在充分利用已有数据的前提下,最终得到更好的缺失值估计结果。

关键词: 矩阵分解, 偏向性, 缺失值估计, 推荐系统, 样本差异性

Abstract: Missing value estimation of sparse matrix is a necessary basic research,which is also particularly important and significant in some practical applications,such as the recommendation system.There are many methods to solve this problem,one of the most effective method to tackle this issue is Matrix Factorization (MF).However,the traditional MF algorithm has some limitations,which can only directly simulate the elements of the sparse matrix by using regression method.But it did not take into account the sample itself,which has different difficulty in regression and should be treated respectively.According to this limitation,this paper proposed a matrix factorization recommendation algorithm based on adaptive weighted samples (AWS-MF).Based on the traditional MF algorithm,the proposed method exploits the differences among the training samples and treats each sample in a bias weights.In order to improve the performance and robustness of our model,the intermediate results are combined together in the final process to obtain the objective predictions.To verify the superiority of the proposed method,the comprehensive experiments were conducted on the real-world data sets.The experiment results demonstrate that the proposed AWS-MF algorithm is able to adaptively re-weight samples according to the differences among them.Moreover,treating the samples respectively can lead to a promising performance in the real-world applications compared to the baseline methods.

Key words: Bias, Matrix Factorization(MF), Missing value estimation, Recommendation system, Sample differences

中图分类号:

TP182

石晓玲, 陈芷, 杨立功, 沈伟. 基于自适应样本权重的矩阵分解推荐算法[J]. 计算机科学, 2019, 46(6A): 488-492. https://doi.org/

SHI Xiao-ling, CHEN Zhi, YANG Li-gong, SHEN Wei. Matrix Factorization Recommendation Algorithm Based on Adaptive Weighted Samples[J]. Computer Science, 2019, 46(6A): 488-492. https://doi.org/

参考文献

[1]BREESE J S,Heckerman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence.Morgan Kaufmann Publishers Inc.,1998:43-52.
[2]HERLOCKER J L,KONSTAN J A,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]∥Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,1999:230-237.
[3]LINDEN G,SMITH B,YORK J.Amazon.com recommendations:Item-to-item collaborative filtering.IEEE Internet Computing,2003,7(1):76-80.
[4]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Procee-dings of the 10th international conference on World Wide Web.ACM,2001:285-295.
[5]KOREN Y,BELL R,VOLINSKY C.Matrix factorization tech-niques for recommender systems.Computer,2009,42(8):30-37.
[6]LEE D D,SEUNG H S.Algorithms for non-negative matrix fac-torization[C]∥Advances in Neural Information Processing Systems.2001:556-562.
[7]PAATERO P,TAPPER U.Positive matrix factorization:A non-negative factor model with optimal utilization of error estimates of data values.Environmetrics,1994,5(2):111-126.
[8]ZHANG S,WANG W,FORD J,et al.Learning from incomplete ratings using non-negative matrix factorization∥Procee-dings of the 2006 SIAM International Conference on Data Mi-ning.Society for Industrial and Applied Mathematics,2006:549-553.
[9]李乐,章毓晋.非负矩阵分解算法综述.电子学报,2008,36(4):737-743.
[10]李改,李磊.基于矩阵分解的协同过滤算法.计算机工程与应用,2011,47(30):4-7.
[11]LUO X,ZHOU M,XIA Y,et al.An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems.IEEE Transactions on Industrial Informatics,2014,10(2):1273-1284.
[12]于玲,吴铁军.集成学习:Boosting 算法综述.模式识别与人工智能,2004,17(1):52-59.
[13]董乐红,耿国华,高原.Boosting 算法综述.计算机应用与软件,2006,23(8):27-29.
[14]SCHAPIRE R E,SINGER Y.Improved boosting algorithms using confidence-rated predictions.Machine Learning,1999,37(3):297-336.
[15]曹莹,苗启广,刘家辰,等.AdaBoost 算法研究进展与展望.自动化学报,2013,39(6):745-758.
[16]HASITE T,ROSSET S,ZHU J,et al.Multi-class adaboost.Statistics and its Interface,2009,2(3):349-360.
[17]RUDER S.An overview of gradient descent optimization algorithms.arXiv preprint arXiv:1609.04747,2016.
[18]HARPER F M,Konstan J A.The movielens datasets:History and context.ACM Transactions on Interactive Intelligent Systems (TiiS),2016,5(4):19.

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