基于泛函网络的组合推荐算法

崔春生

系统工程理论与实践 ›› 2014, Vol. 34 ›› Issue (4) : 1034-1042.

PDF(976 KB)
PDF(976 KB)
系统工程理论与实践 ›› 2014, Vol. 34 ›› Issue (4) : 1034-1042. DOI: 10.12011/1000-6788(2014)4-1034
论文

基于泛函网络的组合推荐算法

    崔春生1,2,3
作者信息 +

Hybrid recommendation based on functional network

    CUI Chun-sheng1,2,3
Author information +
文章历史 +

摘要

在研究组合算法的基础上,提出一种基于泛函网络实现前融合推荐算法. 探讨Vague集条件下推荐算法的前融合问题,给出了基于泛函网络构架实现前融合组合推荐算法的一般过程. 通过推荐系统泛函拓朴结构的建立,利用泛函神经元的自学习、自组织和自适应能力,进一步优化推荐结果,较大地提高了系统的推荐准确度. 最后,将算法应用于Movielens推荐系统中,计算机仿真实验结果表明,本文提出的基于泛函网络实现前融合推荐算法是有效的、可靠的.

Abstract

Based on the research of hybrid recommendation, model hybrid recommendation algorithm is provided with functional network. Meanwhile, how to get the model hybrid recommendation with vague set is discussed, so the general process of model hybrid recommendation using functional networks is studied. The topology of a functional network is established to improve the accuracy of the system's recommendation by using the neuron's self-learning, self-organizing and adaptive ability. Finally, the algorithm is used in Movielens recommender system. Computer simulation and experimental results show that the algorithm is effective and reliability.

关键词

推荐系统 / 组合推荐算法 / 电子商务 / 泛函网路 / Vague集

Key words

hybrid recommendation / recommendation system / e-commerce / functional network / vague set

引用本文

导出引用
崔春生. 基于泛函网络的组合推荐算法. 系统工程理论与实践, 2014, 34(4): 1034-1042 https://doi.org/10.12011/1000-6788(2014)4-1034
CUI Chun-sheng. Hybrid recommendation based on functional network. Systems Engineering - Theory & Practice, 2014, 34(4): 1034-1042 https://doi.org/10.12011/1000-6788(2014)4-1034
中图分类号: TP311   

参考文献

[1] Resnick P, Varian H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3): 56-58.
[2] 许海玲,吴潇,李晓东,等. 互联网推荐系统比较研究[J]. 软件学报, 2009, 20(2): 350-362.Xu Hailing, Wu Xiao, Li Xiaodong, et al. Comparison study of internet recommendation system[J]. Journal of Software, 2009, 20(2): 350-362.
[3] 程岩. 电子商务中面向延迟购买行为的易逝品动态捆绑策略[J]. 系统工程理论与实践, 2011, 31(10): 1892-1902.Cheng Yan. Delay buying behavior-oriented perishable products dynamic bundling strategy in e-commerce setting[J]. Systems Engineering—Theory & Practice, 2011, 31(10): 1892-1902.
[4] 王国霞, 刘贺平. 个性化推荐系统综述[J]. 计算机工程与应用, 2012, 48(7): 66-76.Wang Guoxia, Liu Heping. Survey of personalized recommendation system[J]. Computer Engineering and Applications, 2012, 48(7): 66-76.
[5] 崔春生. 基于集团序方法的推荐系统输出[J]. 系统工程理论与实践, 2013, 33(7): 1845-1851.Cui Chunsheng. Output of recommender systems based on aggregative rank[J]. Systems Engineering—Theory & Practice, 2013, 33(7): 1845-1851.
[6] 崔春生,吴祈宗. 基于Vague集的内容推荐算法研究[J]. 计算机应用研究, 2010, 27(6): 2108-2110.Cui Chunsheng, Wu Qizong. Research on content-based recommendation based on Vague sets[J]. Application Research of Computers, 2010, 27(6): 2108-2110.
[7] 崔春生,齐延信,田哲,等. 基于Vague值的电子商务推荐系统及其相似度研究[J]. 图书情报工作, 2012, 56(7): 130-134.Cui Chunsheng, Qi Yanxin, Tian Zhe, et al. Recommender Systems of EC and its similarity based on vague value[J]. Library and Information Service, 2012, 56(7): 130-134.
[8] 崔春生,李光,吴祈宗. 基于Vague集的电子商务推荐系统研究[J]. 计算机工程与应用, 2011, 47(10): 237-239.Cui Chunsheng, Li Guang, Wu Qizong. Research on recommender systems of electric commerce based on Vague sets[J]. Computer Engineering and Applications, 2011, 47(10): 237-239.
[9] 李杰,徐勇,王云峰,等. 面向个性化推荐的强关联规则挖掘[J]. 系统工程理论与实践, 2009, 29(8): 144-152.Li Jie, Xu Yong, Wang Yunfeng, et al. Strongest association rules mining for personalized recommendation[J]. Systems Engineering—Theory & Practice, 2009, 29(8): 144-152.
[10] 洪文兴,翁洋,朱顺痣,等. 垂直电子商务网站的混合型推荐系统[J].系统工程理论与实践, 2010, 30(5): 928-935.Hong Wenxing, Weng Yang, Zhu Shuzhi, et al. Hybrid recommender system for vertical e-commerce website[J]. Systems Engineering—Theory & Practice, 2010, 30(5): 928-935.
[11] 朱岩,林泽楠. 电子商务中的个性化推荐方法评述[J]. 中国软科学, 2009(2): 183-192.Zhu Yan, Lin Zenan. A review of e-business recommendation system[J]. China Soft Science, 2009(2): 183-192.
[12] Claypool M, Gokhale A, Miranda T, et al. Combining content-based and collaborative filters in an online newspaper[C]// Proceedings of ACM SIGIR Workshop on Recommender Systems. Menlo Park: AAAI Press, 1999: 1-11.
[13] Pazzani M. A framework for collaborative, content-based, and demographic filtering[J]. Artificial Intelligence Review, 1999, 13(5-6): 393-408.
[14] Billsus D, Pazzani M. User modeling for adaptive news access[J]. User Modeling and User-Adapted Interaction, 2000, 10(2-3): 147-180.
[15] Good N, Schafer J B, Konstan J A, et al. Combining collaborative filtering with personal agents for better recommendations[C]// AAAI/IAAI. Menlo Park: AAAI Press, 1999: 439-446.
[16] Melville P, Mooney R J, Nagarajan R. Content-boosted collaborative filtering for improved recommendations[C]// AAAI/IAAI. Menlo Park: AAAI Press, 2002: 187-192.
[17] Soboroff I, Nicholas C. Combining content and collaboration in text filtering[C]// Proceedings of the IJCAI. Menlo Park: AAAI Press, 1999, 99: 86-91.
[18] Basu C, Hirsh H, Cohen W. Recommendation as classification: Using social and content based information in recommendation[C]// AAAI/IAAI. Menlo Park: AAAI Press, 1998: 714-720.
[19] Ansari A, Essegaier S, Kohli R. Internet recommendations systems[J]. Journal of Marketing Research, 2000, 37(3): 363-375.
[20] 孙立莹. 基于组合推荐技术的个性化学习资料推荐的研究[D]. 大连: 大连海事大学, 2010.Sun Liying. Research on learning materials personalized recommendation based on combination recommended technology[D]. Dalian: Dalian Maritime University, 2010.
[21] 李聪,梁昌勇. 基于n序访问解析逻辑的协同过滤冷启动消除方法[J]. 系统工程理论与实践, 2012, 32(7): 1537-1545.Li Cong, Liang Changyong. Cold-start eliminating method of collaborative filtering based on n-sequence access analytic logic[J]. Systems Engineering—Theory & Practice, 2012, 32(7): 1537-1545.
[22] 杨毅,王晓荣,胡迎春. 基于客户/项目的聚类协同过滤组合推荐算法研究[J]. 广西工学院学报, 2011, 22(4): 74-78.Yang Yi, Wang Xiaorong, Hu Yingchun. Researches of collaborative filtering recommendation algorithm based on user and item clustering combination[J]. Journal of Guangxi University of Technology, 2011, 22(4): 74-78.
[23] 郁雪,李敏强. 基于PCA-SOM的混合协同过滤模型[J]. 系统工程理论与实践, 2010, 30(10): 1850-1854.Yu Xue, Li Minqiang. Effective hybrid collaborative filtering model based on PCA-SOM[J]. Systems Engineering—Theory & Practice, 2010, 30(10): 1850-1854.
[24] 姜维,庞秀丽. 面向数据稀疏问题的个性化组合推荐研究[J]. 计算机工程与应用, 2012, 48(21): 21-26.Jiang Wei, Pang Xiuli. Research on personal hybrid recommendation overcoming data sparse problem[J]. Computer Engineering and Applications, 2012, 48(21): 21-26.
[25] Castillo E. Functional networks[J]. Neural Processing Letters, 1998, 7: 151-159.
[26] Castillo E, Cobo A, Manuel Gutierrez J. Functional networks with applications[M]. Kluwer Academic Publisher, 1999.
[27] Castillo E, Hadi A S, Lacruz B, et al. Semiparametric nonlinear regression and transformation using functional networks[J]. Computational Statistics & Data Analysis, 2001, 52(4): 2129-2157.
[28] Castillo E, Cobo A, Manuel Gutierrez J, et al. Working with differential, functional and difference equation using functional networks[J]. Applied Mathematical Modeling, 1999, 23(2): 89-107.
[29] Castillo E, Manuel Gutierrez J. Nonlinear time series modeling and prediction using functional networks. Extracting information masked by chaos[J]. Physics Letters A, 1998, 244(1): 71-84.
[30] 李洪兴. 数学神经网络(I) —— 神经网络的插值机理[J]. 北京师范大学学报:自然科学版, 1996, 32(4): 452-459.Li Hongxing. Mathematical neural networks (I)—Interpolation mechanism of mathematical neural network[J]. Journal of Beijing Normal University: Natural Science, 1996, 32(4): 452-459.
[31] 李春光,廖晓峰,何松柏. 非线性系统辨识的一种泛函网络方法[J]. 系统工程与电子技术, 2001, 23(11): 50-53.Li Chunguang, Liao Xiaofeng, He Songbai. Functional network method for the identification of nonlinear systems[J]. Systems Engineering and Electronics, 2001, 23(11): 50-53.
[32] 李卫斌,焦李成. 三类可分离交换性泛函网络模型[J]. 上海大学学报:自然科学版, 2003, 9(4): 347-350.Li Weibin, Jiao Licheng. Three models of commutable and separable functional network[J]. Journal of Shanghai University: Natural Science, 2003, 9(4): 347-350.
[33] 戴祯杰,农正,周永权. 多项式泛函网络运算模型及应用[J]. 计算机工程与应用, 2005, 41(21): 49-51.Dai Zhenjie, Nong Zheng, Zhou Yongquan. Polynomial functional networks computing model and application[J]. Computer Engineering and Applications, 2005, 41(21): 49-51.
[34] 周永权. 泛函网络理论及其学习算法研究[D]. 西安: 西安电子科技大学, 2006.Zhou Yongquan. Functional network theory and learning algorithms[D]. Xi'an: Xidian University, 2006.
[35] 周永权,赵斌. 泛函网络神经元构造理论与方法[J]. 计算机科学, 2008, 35(7): 122-125.Zhou Yongquan, Zhao Bin. Functional network neurons construct theory and method[J]. Computer Science, 2008, 35(7): 122-125.
[36] 崔强,武春友,匡海波. BP-DEMATEL在空港竞争力影响因素识别中的应用[J]. 系统工程理论与实践, 2013, 33(6): 1471-1478.Cui Qiang, Wu Chunyou, Kuang Haibo. Influencing factors research of air ports competitiveness based BP-DEMATEL model[J]. Systems Engineering—Theory & Practice, 2013, 33(6): 1471-1478.
[37] 罗洪方,周永权,谢竹诚. 一种基于进化泛函网络的建模与函数逼近方法[J]. 计算机科学, 2010, 37(7): 200-204.Luo Hongfang, Zhou Yongquan, Xie Zhucheng. Modeling and function approach based on evolutionary functional networks[J]. Computer Science, 2010, 37(7): 200-204.
[38] 周永权,赵斌. 泛函网络模型及应用研究综述[J]. 电子科技大学学报, 2010, 39(6): 803-809.Zhou Yongquan, Zhao Bin. Progress of functional networks and their applications[J]. Journal of University of Electronic Science and Technology of China, 2010, 39(6): 803-809.
[39] 吕咏梅. 泛函网络新模型及其学习算法研究[D]. 南宁: 广西民族大学, 2008.Lü Yongmei. New models and learning algorithms for functional network[D]. Nanning: Guangxi University for Nationalities, 2008.

基金

河南科技厅基础与前沿技术研究项目(132300410011);河南省科技厅软科学项目(142400410313);河南省社科规划办项目(2013BJJ061);河南省教育厅科学技术研究重点项目(14A630013)
PDF(976 KB)

375

Accesses

0

Citation

Detail

段落导航
相关文章

/