基于融合GMM聚类与FOA-GRNN模型的推荐算法

doi:10.11959/j.issn.2096-109x.2018097

Abstract

Abstract:

Aiming at the problem of low recommendation accuracy caused by sparse data in traditional item-based recommendation algorithm,a recommendation algorithm based on GMM clustering and FOA-GRNN model is proposed.The algorithm firstly uses Gaussian mixture model (GMM) to cluster the item features,then constructs the score matrix according to the clustering results,and fills the score matrix with slope one algorithm.Finally,the user's score based on similarity prediction is taken as input,and the final score is output through FOA-GRNN model.Experimental results based on movielens-2k dataset show that the proposed algorithm can deal with highly sparse data better and has better recommendation accuracy than the other three algorithms.

Key words: recommendation algorithm, GMM clustering, FOA, GRNN, Slope One algorithm

CLC Number:

TP311

Yipeng LI,Yeli RUAN,Jie ZHANG. Recommendation algorithm based on GMM clustering and FOA-GRNN model[J]. Chinese Journal of Network and Information Security, 2018, 4(12): 25-31.

Figures/Tables 7

References 16

[1]	DESHPANDE M , KARYPIS G . Item-based top-N,recommendation algorithms[M]. ACM, 2004.
[2]	RENDLE S , FREUDENTHALER C , GANTNER Z ,et al. BPR:Bayesian personalized ranking from implicit feedback[M]. AUAI Press, 2009: 452-461.
[3]	OCEPEK U , RUGELJ J , BOSNI? Z . Improving matrix factorization recommendations for examples in cold start[J]. Expert Systems with Applications, 2015,42(19): 6784-6794.
[4]	张锋, 常会友 . 使用BP神经网络缓解协同过滤推荐算法的稀疏性问题[J]. 计算机研究与发展, 2006,43(4): 667-672.
	ZHANG F , CHANG H Y . Employing bp neural networks to alleviate the sparsity issue in colaborative filtering recommendation algorithms[J]. Journal of Computer Research and Development, 2006,43(4): 667-672.
[5]	詹增荣, 曾青松 . 基于协方差矩阵表示的图像集匹配[J]. 湖南师范大学自然科学学报, 2015,38(4): 74-79.
	ZHAN Z R , ZENG .Qing-song image set matching based on covariance matrix[J]. Journal of Natural Science of Hunan Normal University, 2015,38(4): 74-79.
[6]	张玉连, 郇思思, 梁顺攀 . 融合机器学习的加权 Slope One 算法[J]. 小型微型计算机系统, 2016,37(8): 1712-1716.
	ZHANG Y L , HUAN S S , LIANG S P .Integrating machine learning into weighted Slope One algorithm[J]. Journal of Chinese Computer Systems, 2016,37(8): 1712-1716.
[7]	郭兰杰, 梁吉业, 赵兴旺 . 融合社交网络信息的协同过滤推荐算法[J]. 模式识别与人工智能, 2016,29(3): 281-288.
	GUO L J , LIANG J Y , ZHAO X W . collaborative filtering recommendation algorithm incorporationg social network information[J]. PR＆AI, 2016,29(3): 281-288.
[8]	张宇, 王文剑, 赵胜男 . 基于正负反馈的 SVM 协同过滤 Top-N推荐算法[J]. 小型微型计算机系统, 2017,38(5): 961-966.
	ZHANG Y , WANG W J , ZHAO S N . SVM collaborative filtering Top-N recommendation algorithm based on positive and negative feedback[J]. Journal of Chinese Computer Systems, 2017,38(5): 961-966.
[9]	庞海龙, 赵辉, 李万龙 ,等. 融合协同过滤的线性回归推荐算法[J]. 计算机应用研究, 2019(5).
	PANG H L , ZHAO H , LI W L ,et al. Linear regression recommendation algorithm with collaborative filtering[J]. Application Research of Computers , 2019(5).
[10]	丁永刚, 李石君, 余伟 ,等. 基于码本聚类和因子分解机的多指标推荐算法[J]. 计算机科学, 2017,44(10): 182-186.
	DING Y G , LI S J , YU W ,et al. Multi-criteria recommendation algorithm based on codebook-clustering and factorization machines[J]. Computer Science, 2017,44(10): 182-186.
[11]	龚敏, 邓珍荣, 黄文明 . 基于用户聚类与Slope One填充的协同推荐算法[J]. 计算机工程与应用, 2018,20(8): 102-110.
	GONG M , DENG Z R , HUANG W M . Collaborative recommendation algorithm based on user clustering and Slope One filling[J]. Computer Engineering and Applications, 2018,20(8): 102-110.
[12]	陶维成, 党耀国 . 基于灰色关联聚类的协同过滤推荐算法[J]. 运筹与管理, 2018(1): 84-88.
	TAO W C , DANG Y G . Collaborative filtering recommendation algorithm based on grey incidence clustering[J]. Operations Research and Management Science, 2018(1): 84-88.
[13]	杨大鑫, 王荣波, 黄孝喜 ,等. 基于最小方差的K-means用户聚类推荐算法[J]. 计算机技术与发展, 2018(1): 104-107.
	YANG D X , WANG R B , HUANG X X ,et al. K-means user clustering recommendation algorithm based on minimum variance[J]. Computer Technology and Development, 2018(1): 104-107.
[14]	LEMIRE D , MACLACHLAN A . Slope One Predictors for online:rating-based collaborative filtering[C]// Proc of SIAM Data Mining. 2005: 471-476
[15]	BREESE J S , HECKERMAN D , KADIE C . Empirical analysis of predictive algorithm for collaborative filtering[R]. MSR-TR-98-12,Redmond,Microsoft, 1998.
[16]	SEHAFER J , FRANKOWSKI D , HERLOCKER J . Collaborative filtering recommender systems[M]. Berlin:Springer. 2007.

Metrics

Recommended 0

No Suggested Reading articles found!

u?a	a₁	A₂	A₃	…	A_M
u₁	3	1
u₂			4		5
u₃	2
#			5
u_l	1	3			1

u?a	a₁	a₂	…	a_i
u₁	1	4	3	?
u₂	2	2	2	?
#	1	2	2	?
u_j	2	4	3	?

Recommendation algorithm based on GMM clustering and FOA-GRNN model

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 16

Related Articles 15

Metrics

Recommended 0

[1]	Liquan CHEN, Yuhang ZHU, Yu WANG, Zhongyuan QIN, Yang MA. New hash function based on C-MD structure and chaotic neural network [J]. Chinese Journal of Network and Information Security, 2023, 9(3): 1-15.
[2]	Benwei HE, Yunfei GUO, Yawen WANG, Qingfeng WANG, Hongchao HU. Software diversification method based on binary rewriting [J]. Chinese Journal of Network and Information Security, 2023, 9(2): 94-103.
[3]	Feng LIU, Jie YANG, Jiayin QI. Survey on blockchain privacy protection techniques in cryptography [J]. Chinese Journal of Network and Information Security, 2022, 8(4): 29-44.
[4]	Zhen GAO, Dongbin ZHANG, Xiao TIAN. Defense scheme for the world state based attack in Ethereum [J]. Chinese Journal of Network and Information Security, 2022, 8(2): 64-72.
[5]	Xueliang SUN, Wei WANG, Junheng HUANG, Guodong XIN, Bailing WANG. Two-stage community detection algorithm based on label propagation [J]. Chinese Journal of Network and Information Security, 2022, 8(2): 139-149.
[6]	Bo ZHAO, Anqi YUAN, Yang AN. Application progress of SGX in trusted computing area [J]. Chinese Journal of Network and Information Security, 2021, 7(6): 126-142.
[7]	Xieli ZHANG, Yuefei ZHU, Chunxiang GU, Xi CHEN. Security protocol code analysis method combining model learning and symbolic execution [J]. Chinese Journal of Network and Information Security, 2021, 7(5): 93-104.
[8]	Ding LI, Yuefei ZHU, Bin LU, Wei LIN. Survey of side channel attack on encrypted network traffic [J]. Chinese Journal of Network and Information Security, 2021, 7(4): 114-130.
[9]	Xiaojiao CHEN, Xianzheng LIN, Nenghai YU. Compression of bitcoin blockchain [J]. Chinese Journal of Network and Information Security, 2021, 7(1): 76-83.
[10]	Pu GENG,Yuefei ZHU. Research on construction of conditional exception code used in branch obfuscation [J]. Chinese Journal of Network and Information Security, 2020, 6(6): 25-34.
[11]	Xiaolin ZHANG,Dawu GU,Chi ZHANG. Issues of identity verification of typical applications over mobile terminal platform [J]. Chinese Journal of Network and Information Security, 2020, 6(6): 137-151.
[12]	Pu GENG,Yuefei ZHU. Review of path branch obfuscation [J]. Chinese Journal of Network and Information Security, 2020, 6(2): 12-18.
[13]	Kai LEI,Fangxing SHU,Lei HUANG,Qichao ZHANG. Research on cross-domain trustable blockchain based decentralized DNS architecture [J]. Chinese Journal of Network and Information Security, 2020, 6(2): 19-34.
[14]	Zijian BAO,Qinghao WANG,Yongxin ZHANG,Bin WANG,Ning LU,Wenbo SHI. Regulatory Bitcoin privacy-preserving mixing service [J]. Chinese Journal of Network and Information Security, 2019, 5(4): 40-51.
[15]	Qingyang DING, Jianming ZHU, Jin ZHANG, Biao SONG, Yanjing XU, Chuanchang JIA, Zheng GAO. Traceability permissioned chain consensus mechanism based on double-layer architecture [J]. Chinese Journal of Network and Information Security, 2019, 5(2): 1-12.