计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 63-69.doi: 10.11896/j.issn.1002-137X.2017.03.016

• 2015全国高性能计算学术年会 • 上一篇    下一篇

基于人口统计学的改进聚类模型协同过滤算法

王媛媛,李翔   

  1. 淮阴工学院计算机与软件工程学院 淮安223003河海大学计算机与信息学院 南京211100,淮阴工学院计算机与软件工程学院 淮安223003河海大学计算机与信息学院 南京211100
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金(61403060),江苏重点研发计划-产业前瞻与共性关键技术(BE2015127),江苏省高校自然科学研究面上项目(15KJB520004),江苏省先进制造技术重点实验室开放基金(HGAMTL-1401),江苏省科技厅产学研联合研究项目(BY2014097), 淮安市科技计划项目(HAG2015060,HAG201602,HAC201601)资助

Study on Improved Clustering Collaborative Filtering Algorithm Based on Demography

WANG Yuan-yuan and LI Xiang   

  • Online:2018-11-13 Published:2018-11-13

摘要: 针对传统基于用户的协同过滤推荐算法在大数据环境下存在评分高维稀疏性、推荐精度低的问题,提出一种基于人口统计学数据与改进聚类模型相结合的协同过滤推荐算法,以提高推荐系统精度和泛化能力。该方法首先通过用户人口统计学数据属性,结合用户-项目评分矩阵计算各个用户间的相似度;然后对用户、项目进行分层近邻传播聚类,根据用户对项目的评分数据计算用户或项目之间的相似性,产生目标用户或项目的兴趣近邻;最后根据兴趣最近邻进行推荐。对Epinions,MovieLents等数据集进行仿真实验,仿真的结果表明, 与传统的协同过滤算法相比, 提出的算法提高了推荐精度,为传统的协同过滤推荐算法提供了参考。

关键词: 协同过滤,人口统计学,聚类,推荐系统

Abstract: The traditional user based collaborative filtering recommendation algorithm in large data environment has the problem of high dimensional sparse and low recommendation accuracy.A collaborative filtering recommendation algorithm based on the combination of demographic data and improved clustering model was proposed to improve the accuracy and generalization ability of the recommendation system.Firstly,this method calculates the similarity among diffe-rent users through the user demographic data attributes and the user-item score matrix.Secondly,hierarchical neighbor clustering of user and project,calculates the similarity between users or items by the user’s score data for the project,and generates interest in a neighbor of a target user or project.Finally,according to the recent interest in the nearest neighbor to recommend.Simulation experiments on Epinions and MovieLents data set,the simulation results show that the proposed algorithm improves the recommendation accuracy compared with the traditional collaborative filtering algorithm,provide reference for the traditional collaborative filtering recommendation algorithm.

Key words: Collaborative filtering,Demography,Clustering,Recommender systems

[1] ZHU Y Y,SUN J.Recommender System:Up to Now [J].Journal of Frontiers of Computer Science and Technology,2015,9(5):513-525.(in Chinese) 朱扬勇,孙婧.推荐系统研究进展[J].计算机科学与探索,2015,9(5):513-525.
[2] SUN T H,LI A N,LI M,et al.Study on distributed improved clustering collaborative filtering algorithm based on Hadoop[J].Computer Engineering and Applications,2015,51(15):124-128.(in Chinese) 孙天昊,黎安能,李明,等.基于Hadoop分布式改进聚类协同过滤推荐算法研究[J].计算机工程与应用,2015,51(15):124-128.
[3] LI G J,CHENG X Q.Research Status and Scientific Thinking of Big Data[J].Bulletin of Chinese Academy of Sciences,2012,27(6):647-657.(in Chinese) 李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域—大数据的研究现状与科学思考[J].中国科学院院刊,2012,27(6):647-657.
[4] MENG X W,JI W Y,ZHANG Y J.A survey Recommendation Systems in Big Data[J].Journal of Beijing University of Posts and Telecommunications,2015,38(2):1-15.(in Chinese) 孟祥武,纪威宇,张玉洁.大数据环境下的推荐系统[J].北京邮电大学学报,2015,38(2):1-15.
[5] LI W H,XU S R.Design and implementation of recommendation system for E-commerce on Hadoop[J].Computer Enginee-ring and Design,2014,35(1):130-143.(in Chinese) 李文海,许舒人.基于Hadoop的电子商务推荐系统的设计与实现[J].计算机工程与设计,2014,35(1):130-143.
[6] GOLDBERG D,NICHOLS D,OKI B M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of the ACM,1992,35(12):61-70.
[7] TANG J,WU S,SUN J M,et al.Cross-domain collaboration recommendation[C]∥Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.USA:ACM,2012:1285-1293.
[8] BURKE R.Hybrid recommender systems:Survey and experiments[J].User Modeling and User-adapted Interaction,2002,12(4):331-370.
[9] JIAO D J.Collaborative filtering algorithm based on user demographics and expert opinions[J].Computer Engineering & Scien-ce,2015,37(1):179-183.(in Chinese) 焦东俊.基于用户人口统计与专家信任的协同过滤算法[J].计算机工程与科学,2015,37(1):179-183.
[10] ZHANG C,CHEN G,WANG H M.Recommendation Model Basedon Blending Recommendation Technology[J].Computer Engineering,2010,36(22):248-250,3.(in Chinese) 张驰,陈刚,王慧敏.基于混合推荐技术的推荐模型[J].计算机工程,2010,36(22):248-250,3.
[11] HE J Y,MA B.Based on Real-Valued Conditional RestrictedBoltzmann Machine and Social Network for Collaborative Filtering[J].Chinese Journal of Computers,2015,8(1):183-195.(in Chinese) 何洁月,马贝.利用社交关系的实值条件受限玻尔兹曼机协同过滤推荐算法[J].计算机学报,2015,8(1):183-195.
[12] WU H C,WANG X J,CHENG Y,et al.Advanced Recommendation Based on Collaborative Filtering and Partition Clustering[J].Journal of Computer Research and Development,2011,8(Suppl.):205-212.(in Chinese) 吴泓辰,王新军,成勇,等.基于协同过滤与划分聚类的改进推荐算法[J].计算机研究与发展,2011,8(Suppl.):205-212.
[13] XU W,DUAN F.Combining clustering and collaborative filtering for implicit recommender system[J].Computer Engineering and Design,2014,5(12):4181-4185.(in Chinese) 许伟,段富.聚类与协同过滤相结合的隐式推荐系统[J].计算机工程与设计,2014,5(12):4181-4185.
[14] LU Z M,FENG J G,FAN D M,et al.Novel partitional clustering algorithm for large data processing[J].System Engineering and Electronics,2014,6(5):1010-1015.(in Chinese) 卢志茂,冯进玫,范冬梅,等.面向大数据处理的划分聚类新方法[J].系统工程与电子技术,2014,6(5):1010-1015.
[15] WU M H,ZHANG H X,JIN C H,et al.Cluster Algorithm Bases on edge Density Distance[J].Computer Science,2014,1(8):245-249.(in Chinese) 吴明晖,张红喜,金苍宏,等.一种基于边缘度密度距的聚类算法[J].计算机科学,2014,1(8):245-249.
[16] LI G,ZHANG Z B,LIU F X,et al.Nonlinear combinatorial collaborative filtering recommendation algorithm[J].Jouranal of Computer Applications,2011,31(11):3063-3067.
[17] LIU X N,YIN M J,LI M T,et al.Hierarchical Affinity Propagation Clustering for Large-scale Data Set[J].Computer Science,2014,1(3):185-188,2.(in Chinese) 刘晓楠,尹美娟,李明涛,等.面向大规模数据的分层近邻传播聚类算法[J].计算机科学,2014,1(3):185-188,2.
[18] ALBERT R,JEONG H H,BARABSI A L.Attack and Error Tolerance of Complex Networks[J].Nature,2000,406:378-382.
[19] WU Y F,WANG H R.Collaborative filtering algorithm using user background information[J].Computer Applications,2008,28(11):2972-2974.(in Chinese) 吴一帆,王浩然.结合用户背景信息的协同过滤推荐算法[J].计算机应用,2008,28(11):2972-2974.
[20] SUN G M,WANG S.Compute adaptive fast recommendation algorithm satisfied user interests drift[J].Application Research of Computers,2013,30(12):3618-3621.(in Chinese) 孙光明,王硕.基于项目兴趣度的协同过滤新算法[J].计算机应用研究,2013,30(12):3618-3621.
[21] KEPHART J,CHESS D.The Vision od Autonomic Computing[J].IEEE Computer Society,2003,6(1):41-50.
[22] Jrvelin K,Keklinen J.Evaluation methods for retrieving highly relevant documents[C]∥Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR’00).ACM,New York,NY,USA,2000:41-48.
[23] CHAPELLE O,METLZER D,ZHANG Y,et al.Expected reciprocal rank for graded relevance[C]∥Proceedings of the 18th ACM Conference on Information and Knowledge Management(CIKM’09).ACM,New York,NY,USA,2009:621-630.
[24] HU Y,KOREN Y,VOLINSKY C.Collaborative filtering forimplicit feedback data sets[C]∥Proceedings of the 2008 Eighth IEEE International Conference on Data Mining(ICDM’08).IEEE Computer Society,Washington,DC,USA,2008:263-272.
[25] GANTNER Z,DRUMOND L,FREUDENTHALER C.Baye-sian personalized ranking for non-uniformly sampled items[C]∥Proceedings of Knowledge Discovery and Data Mining (KDD) Cup and Workshop.2011.
[26] WEIMER M,KARATZOGLOU A,SMOLA A.Improving maxi-mum margin matrix factorization[J].Mach.Learn,2008,72(3):263-276.
[27] RENDLE S,FREUDENTHALER C,GANTNER Z.Bayesianpersonalized ranking from implicit feedback [C]∥Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence(UAI’09).AUAI Press,Arlington,Virginia,United States,2009:452-461.
[28] SALAKHUTDINOV R,MNIH A.Probabilistic matrix factorization[C]∥Proceedings of Advances in Neural Information Processing Systems(NIPS’08).2008:1257-1264.
[29] PATEREK A.Improving regularized singular value decomposition for collaborative filtering [C]∥Proceedings of Knowledge Discovery and Data Mining (KDD) Cup and Work Shop.2007:39-42.
[30] LIU W,WU C,Feng B,et al.Conditional preference in recommender systems [J].Expert Syst.Appl.,2015,42(2):774-788.

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