面向用户的个性化产品服务系统协同过滤推介方法

doi:10.13229/j.cnki.jdxbgxb.20210964

Abstract

Abstract:

An improved collaborative filtering recommendation model is proposed to recommend the new product service system for users quickly and accurately. Firstly， the target user neighbor set is determined. Aiming at the problem of data cold start in the traditional collaborative filtering algorithm， a method combining user attribute similarity and user experience similarity is proposed， and Jaccard coefficient， average score correction coefficient and popular coefficient are introduced to improve the accuracy of user experience similarity. Then， the similarity set of new product service system is determined. An improved Pearson cosine similarity algorithm based on product service system attributes is proposed to solve the problem that the traditional project-based collaborative filtering algorithm ignores the similarity constraints of project attributes， and BP neural network is used to obtain the objective weight of each attribute under different product service systems， which improves the reliability of attribute importance. Finally， the recommendation guideline to judge whether the new product service system can be recommended to target user is constructed. Taking the tractor service system recommendation as an example， the feasibility and effectiveness of recommendation model are verified.

Key words: agricultural engineering, product service system, collaborative filtering, Pearson correlation coefficient, cosine similarity, back propagation neural network, recommendation

CLC Number:

TP391.3

Feng LYU,Nian LI,Zhuang-zhuang FENG,Yang-hang ZHANG. Method of collaborative filtering recommendation of personalized product-service system based on user[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1935-1942.

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References 17

1	孙林岩,李刚,江志斌,等. 21世纪的先进制造模式——服务型制造[J]. 中国机械工程,2007,18(19): 2307-2312.
	Sun Lin-yan, Li Gang, Jiang Zhi-bin, et al. Service-embedded manufacturing: advanced manufacturing paradigm in 21st Century[J]. China Mechanical Engineering, 2007, 18(19): 2307-2312.
2	Xu G Y, Zhang R B, Xu S X, et al. Personalized multimodal travel service design for sustainable intercity transport[J]. Journal of Cleaner Production, 2021,308: No.127367.
3	Zheng P, Lin T J, Chen C H, et al. A systematic design approach for service innovation of smart product-service systems[J]. Journal of cleaner production, 2018, 201(10): 657-667.
4	Jiang L L, Cheng Y T, Yang L. A trust-based collaborative filtering algorithm for E-commerce recommendation system[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(8): 3023-3024.
5	高玉凯,王新华,郭磊,等. 一种基于协同矩阵分解的用户冷启动推荐算法[J]. 计算机研究与发展,2017, 54(8): 1813-1823.
	Gao Yu-kai, Wang Xin-hua, Guo Lei, et al. Learning to recommend with collaborative matrix factorization for new users[J]. Computer Research and Development, 2017, 54(8): 1813-1823.
6	任永功,王瑞霞,张志鹏,等. 基于社交网络能量扩散的协同过滤推荐算法[J]. 模式识别与人工智能, 2021, 34(6):561-571.
	Ren Yong-gong, Wang Rui-xia, Zhang Zhi-peng, et al. Collaborative filtering recommendation algorithm based on energy diffusion in social network[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(6): 561-571.
7	张志鹏,张尧,任永功. 基于覆盖约简的个性化协同过滤推荐方法[J]. 模式识别与人工智能, 2019, 32(7): 607-614.
	Zhang Zhi-peng, Zhang Yao, Ren Yong-gong. Personalized collaborative filtering recommendation approach based on covering reduction[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(7): 607-614.
8	陈碧毅,黄玲,王昌栋,等. 融合显式反馈与隐式反馈的协同过滤推荐算法[J]. 软件学报,2020(3): 794-805.
	Cheng Bi-yi, Huang Ling, Wang Chang-dong, et al. Explicit and implicit feedback based collaborative filtering algorithm[J]. Journal of Software, 2020(3): 794-805.
9	肖诗涛,邵蓥侠,宋卫平,等. 面向协同过滤推荐的新型混合评分函数[J]. 计算机科学,2021, 48(3): 113-118.
	Xiao Shi-tao, Shao Ying-xia, Song Wei-ping, et al. Hybrid score function for collaborative filtering recommendation[J]. Computer Science, 2021, 48(3): 113-118.
10	张润莲,张瑞,武小年,等. 基于混合相似度和差分隐私的协同过滤推荐算法[J]. 计算机应用研究,2021,38(8): 2334-2339.
	Zhang Rui-lian, Zhang Rui, Wu Xiao-nian, et al. Collaborative filtering recommendation algorithm based on mixed similarity and differential privacy[J]. Application Research of Computers, 2021, 38(8): 2334-2339.
11	Feng L, Zhao Q C, Zhou C Q, Improving performances of Top-N recommendations with co-clustering method[J]. Expert Systems with Applications, 2020, 143: 1388-1405.
12	Zhu B, Jin W L, Li L. Evaluation of brake pedal feeling based on subjective and objective comprehensive weighting method[J]. Automotive Engineering, 2021, 43(5): 697-704.
13	Yin Y, Zhang Y. Environmental pollution evaluation of urban rail transit construction based on entropy weight method[J]. Nature Environment and Pollution Technology, 2021, 20(2): 819-824.
14	Liu D G. An entropy weight method of collaborative degree model between producer service industry and manufacturing industry[J]. International Journal of Circuits, Systems and Signal Processing, 2021, 15: 672-676.
15	Xu Z Y, Gao Y B. Kong X Y. Novel Dual-purpose algorithm for principal and minor component analysis[J]. 2020, 8: 31530-31538.
16	陈耶拉,耿秀丽. 基于改进协同过滤的个性化产品服务系统方案推荐[J]. 计算机集成制造系统, 2021, 27(1):240-248.
	Chen Ye-la, Geng Xiu-li. Recommendation of personalized product-service system scheme based on improved collaborative filtering[J]. Computer Integrated Manufacturing Systems, 2021, 27(1): 240-248.
17	吴佳婧,贺嘉楠,王越群,等. 基于项目属性分类的协同过滤算法研究[J]. 吉林大学学报:信息科学版, 2018, 36(4): 470-474.
	Wu Jia-jing, He Jia-nan, Wang Yue-qun, et al. Research on collaborative filtering algorithm based on items' attribute categories[J]. Journal of Jilin University(Information Science Edition), 2018,36(4): 470-474.

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用户	P₁	P₂	P₃	P₄	用户	P₁	P₂	P₃	P₄
U₁	5	2	5	/	U₅₀	3	/	5	4
U₂	/	/	4	4	U₅₈	3	3	4	/
U₃	2	/	5	3	U₆₄	/	2	5	4
U₆	/	3	5	/	U₆₇	4	/	5	5
U₇	3	/	4	4	U₇₃	5	1	4	/
U₁₂	/	2	5	3	U₇₅	3	/	5	4
U₁₆	4	3	4	/	U₇₈	4	2	5	/
U₁₉	4	/	4	/	U₈₄	4	/	4	3
U₂₂	/	3	5	4	U₈₈	2	3	5	/
U₂₇	4	2	4	/	U₈₉	3	/	5	/
U₃₀	5	/	4	/	U₉₆	4	2	5	3
U₃₄	4	1	5	2	U₉₇	3	/	4	4
U₃₈	3	/	4	4	U₉₈	3	2	4	1
U₄₀	5	3	4	4	U₉₉	3	/	5	2
U₄₅	4	/	4	/	U₁₀₀	4	/	5	4

	m₁	m₂	m₃	m₄	m₅	m₆	m₇	m₈	m₉	m₁₀
P₁	4.5	3.5	4.0	4.2	2.8	3.2	2.5	2.0	2.3	2.4
P₂	3.1	3.5	3.8	4.1	4.5	2.9	3.0	3.7	3.1	2.5
P₃	3.4	4.2	4.6	4.7	2.9	3.8	2.7	3.6	2.6	3.7
P₄	2.7	4.8	4.1	4.0	3.0	3.7	2.3	2.2	3.1	2.9

	m₁	m₂	m₃	m₄	m₅	m₆	m₇	m₈	m₉	m₁₀
P₁	0.1233	0.1063	0.0406	0.1245	0.1186	0.1589	0.1134	0.1823	0.0273	0.0047
P₂	0.0577	0.1221	0.1358	0.1360	0.1389	0.0118	0.0801	0.1385	0.0848	0.1247
P₃	0.0775	0.1129	0.1028	0.1138	0.0556	0.1093	0.1124	0.0924	0.1085	0.0978
P₄	0.1051	0.1210	0.1023	0.0975	0.1215	0.0872	0.0627	0.1021	0.0735	0.1439
P_new	0.1024	0.1170	0.0666	0.1526	0.0360	0.1146	0.1265	0.1027	0.1321	0.0496

	m₁	m₂	m₃	m₄	m₅	m₆	m₇	m₈	m₉	m₁₀
P₁	0.5549	0.3720	0.1624	0.5603	0.3558	0.5085	0.2835	0.3646	0.6020	0.0113
P₂	0.1558	0.4884	0.5568	0.5440	0.4167	0.0437	0.1842	0.3047	0.2629	0.3616
P₃	0.3100	0.4742	0.3701	0.5349	0.1612	0.4153	0.3035	0.3326	0.4232	0.2347
P₄	0.3258	0.4598	0.3887	0.3413	0.3645	0.2529	0.1881	0.3880	0.2279	0.3598
P_new	0.4096	0.4680	0.1998	0.7630	0.1080	0.4584	0.3795	0.3081	0.6605	0.1984

Method of collaborative filtering recommendation of personalized product-service system based on user

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