基于矩阵填充与改进PSO算法的多标准协同过滤

doi:10.19678/j.issn.1000-3428.0052783

计算机工程 ›› 2019, Vol. 45 ›› Issue (12): 176-181,200. doi: 10.19678/j.issn.1000-3428.0052783

基于矩阵填充与改进PSO算法的多标准协同过滤

叶莉¹, 吴春明¹, 强保华², 谢武³

1. 西南大学计算机与信息科学学院, 重庆 400715;
2. 广西密码学与信息安全重点实验室, 广西桂林 541004;
3. 广西高校云计算与复杂系统重点实验室, 广西桂林 541004

收稿日期:2018-09-29 修回日期:2018-12-19 发布日期:2018-12-26
作者简介:叶莉(1993-),女,硕士,主研方向为智能推荐系统;吴春明,副教授、博士;强保华、谢武,博士。
基金资助:
国家自然科学基金（61762025）；西南大学自然科学基金（SWU112017）；广西密码学与信息安全重点实验室项目（GCIS201709）；广西云计算与大数据合作创新中心项目（YD16E04）；广西高校云计算与复杂系统重点实验室项目（14106，15204）。

Multi-Criteria Collaborative Filtering Based on Matrix Filling and Improved PSO Algorithm

YE Li¹, WU Chunming¹, QIANG Baohua², XIE Wu³

1. College of Computer and Information Science, Southwest University, Chongqing 400715, China;
2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin, Guangxi 541004, China;
3. Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex System, Guilin, Guangxi 541004, China

Received:2018-09-29 Revised:2018-12-19 Published:2018-12-26

摘要/Abstract

摘要： 在多标准协同过滤中，存在稀疏性处理方法单一以及传统粒子群优化（PSO）算法早熟、易陷入局部最优等问题。为此，基于矩阵填充及改进PSO算法，提出一种多标准协同过滤模型。采用矩阵填充方法对稀疏数据的缺失部分进行估算，以避免降维方法对原始数据信息造成损失，同时结合高斯算子快速收敛的优势以及遗传算子对生物进化模拟的有效性对PSO算法进行改进，聚合多标准评分生成TopN推荐列表。实验结果表明，与基于标准PSO算法以及基于遗传算子改进PSO算法的模型相比，该模型的评分预测准确度较优，能为个性化推荐提供有效的支持。

关键词: 多标准协同过滤, 矩阵填充, 改进粒子群优化算法, 高斯算子, 遗传算子

Abstract: In multi-criteria collaborative filtering,current methods for sparsity processing are relatively insufficient,and the traditional Particle Swarm Optimization(PSO) algorithm has the drawbacks of premature convergence and easy to fall into local optimum.So,we propose a multi-criteria collaborative filtering model based on matrix filling and improved PSO algorithm.First,we use matrix filling method to estimate the missing part of the sparse data,so as to avoid the loss of the original data information caused by the dimensionality reduction method.Then,we present an improved PSO algorithm,which fully combines the advantages of fast convergence of Gaussian operators and the effectiveness of genetic operators on biological evolution simulation.Finally,a TopN recommendation list is generated through aggregated multi-criteria score.Experimental results show that compared with the P_MCCF model and the GIP_MCCF model,the proposed model has better scoring prediction accuracy,which can provide effective support for personalized recommendations.

Key words: multi-criteria collaborative filtering, matrix filling, improved Particle Swarm Optimization(PSO) algorithm, Gaussian operator, genetic operator

中图分类号:

TP391

叶莉, 吴春明, 强保华, 谢武. 基于矩阵填充与改进PSO算法的多标准协同过滤[J]. 计算机工程, 2019, 45(12): 176-181,200.

YE Li, WU Chunming, QIANG Baohua, XIE Wu. Multi-Criteria Collaborative Filtering Based on Matrix Filling and Improved PSO Algorithm[J]. Computer Engineering, 2019, 45(12): 176-181,200.

http://www.ecice06.com/CN/Y2019/V45/I12/176

图/表 8

20191214133051

20191214133054

20191214133056

20191214133059

20191214133101

20191214133105

20191214133108

20191214133111

参考文献

[1] RICCI F,ROKACH L,SHAPIRA B,et al.Recommender systems handbook[M].Berlin,Germany:Springer,2011.
[2] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[3] NILASHI M,IBRAHIM O B,ITHNIN N,et al.A multi-criteria collaborative filtering recommender system for the tourism domain using EM and PCA-ANFIS[J].Electronic Commerce Research and Applications,2015,14(6):542-562.
[4] NILASHI M,IBRAHIM O B,ITHNIN N,et al.A multi-criteria recommendation system using dimensionality reduction and neuro-fuzzy techniques[J].Soft Computing,2015,19(11):3173-3207.
[5] NILASHI M,FARD K B,RAHMANI M,et al.A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques[J].Computers and Industrial Engineering,2017,109:357-368.
[6] ADOMAVICIUS G,KWON Y O.New recommendation techniques for multi-criteria rating systems[J].IEEE Intelligent Systems,2007,22(3):48-55.
[7] BILGE A,KALELI C.A multi-criteria item-based collaborative filtering framework[C]//Proceedings of International Joint Conference on Computer Science and Software Engineering.Washington D.C.,USA:IEEE Press,2014:18-22.
[8] JHALANI T,KANT V,DWIVEDI P.A linear regression approach to multi-criteria recommender system[C]//Proceedings of International Conference on Data Mining and Big Data.Berlin,Germany:Springer,2016:235-243.
[9] NILASHI M,JANNACH D,IBRAHIM O B,et al.Clustering and regression-based multi-criteria collaborative filtering with incremental updates[J].Information Sciences,2015,293:235-250.
[10] NILASHI M,ESFAHANI M D,ROUDBARAKI M Z,et al.A multi-criteria collaborative filtering recommender system using clustering and regression techniques[J].Journal of Soft Computing and Decision Support Systems,2016,3(5):24-30.
[11] CHOUDHARY P,KANT V,DWIVEDI P.A particle swarm optimization approach to multi criteria recommender system utilizing effective similarity measures[C]//Proceedings of the 9th International Conference on Machine Learning and Computing.New York,USA:ACM Press,2017:81-85.
[12] HASSAN M,HAMADA M.Genetic algorithm approaches for improving prediction accuracy of multi-criteria recommender systems[J].International Journal of Computational Intelligence Systems,2018,11(1):146-162.
[13] ZHANG Xiaolin,FU Yingzi,CHU Peixiao.Application of Jaccard similarity coefficient in recommender system[J].Computer Technology and Development,2015,25(4):158-161.(in Chinese)张晓琳,付英姿,褚培肖.杰卡德相似系数在推荐系统中的应用[J].计算机技术与发展,2015,25(4):158-161.
[14] PAN Taotao,WEN Feng,LIU Qinrang.Collaborative filtering recommendation algorithm based on rating matrix filling and item predictability[J].Acta Automatica Sinica,2017,43(9):1597-1606.(in Chinese)潘涛涛,文峰,刘勤让.基于矩阵填充和物品可预测性的协同过滤算法[J].自动化学报,2017,43(9):1597-1606.
[15] KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Washington D.C.,USA:IEEE Press,1995:1942-1948.
[16] WU Runxiu,SUN Hui,ZHU Degang,et al.Particle swarm optimization algorithm based on optimal particle guidance and Gauss perturbance[J].Journal of Chinese Computer Systems,2016,37(1):146-151.(in Chinese)吴润秀,孙辉,朱德刚,等.具有高斯扰动的最优粒子引导粒子群优化算法[J].小型微型计算机系统,2016,37(1):146-151.
[17] RATNAWEERA A,HALGAMUGE S K,WATSON H C.Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients[M].Washington D.C.,USA:IEEE Press,2004.
[18] YE Li,WU Chunming,QIANG Baohua,et al.Multi-criteria collaborative filtering based on matrix filling[C]//Proceedings of the 4th International Conference on Computer and Communications.Washington D.C.,USA:IEEE Press,2018:2553-2558.

选择文件类型/文献管理软件名称

选择包含的内容

基于矩阵填充与改进PSO算法的多标准协同过滤

Multi-Criteria Collaborative Filtering Based on Matrix Filling and Improved PSO Algorithm

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	张岐山, 李可, 林小榕. 基于时间效应的兴趣点推荐混合模型[J]. 计算机工程, 2019, 45(8): 203-209.
[2]	余玛俐,张海. 基于低秩矩阵填充与全变分约束的HDR成像[J]. 计算机工程, 2019, 45(4): 262-266,274.
[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(1): 36-40.
[4]	张步忠,程玉胜,王一宾. 求解N皇后问题的片上多核并行混合遗传算法[J]. 计算机工程, 2015, 41(7): 199-203.
[5]	赵慧娟, 孙文辉. 基于退火遗传算法的单元测试方法[J]. 计算机工程, 2013, 39(1): 49-53.
[6]	张水平, 邬海艳. 基于元胞自动机遗传算法的云资源调度[J]. 计算机工程, 2012, 38(11): 11-13.
[7]	巩固;黄永清;郝国生. 交互式遗传算法的改进方法及应用[J]. 计算机工程, 2009, 35(1): 165-167.
[8]	殷文. 改进的并行遗传模型的构建及应用[J]. 计算机工程, 2008, 34(4): 203-206.
[9]	李蔚;邓沌华;黄德修. 基于动态算子遗传算法的光网络阻塞率的快速计算[J]. 计算机工程, 2007, 33(13): 107-108.
[10]	周子康;杨　衡;唐万生. 组合投资双目标概率准则模型及GASS II算法求解[J]. 计算机工程, 2006, 32(19): 185-187.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于矩阵填充与改进PSO算法的多标准协同过滤

Multi-Criteria Collaborative Filtering Based on Matrix Filling and Improved PSO Algorithm

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献

相关文章 10

编辑推荐

Metrics

本文评价