计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 52-57.doi: 10.11896/j.issn.1002-137X.2017.12.010

• 第四届CCF大数据学术会议 • 上一篇    下一篇

一种基于Bhattacharyya系数和项目相关性的协同过滤算法

臧雪峰,刘天琦,孙小新,冯国忠,张邦佐   

  1. 东北师范大学计算机科学与信息技术学院 长春130117,东北师范大学计算机科学与信息技术学院 长春130117,东北师范大学计算机科学与信息技术学院 长春130117,东北师范大学计算机科学与信息技术学院 长春130117,东北师范大学计算机科学与信息技术学院 长春130117
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(71473035,11501095),吉林省科技厅重点攻关项目(20150204040GX),吉林省发改委项目(2015Y055),东北师范大学自然科学基金项目(2014015KJ004)资助

Collaborative Filtering Algorithm Based on Bhattacharyya Coefficient and Item Correlation

ZANG Xue-feng, LIU Tian-qi, SUN Xiao-xin, FENG Guo-zhong and ZHANG Bang-zuo   

  • Online:2018-12-01 Published:2018-12-01

摘要: 在大数据时代,为了满足用户的信息需求,个性化推荐系统得到了广泛应用。协同过滤是一种简单有效的推荐算法。然而,许多传统的相似度计算方法仅仅基于用户的共同评分值,且不适用于稀疏数据环境,因此提出了一种新的基于Bhattacharyya系数的相似度方法。该方法使用了所有用户对项目的评分信息,不仅可以通过用户的评分行为获得用户的相似兴趣特征,而且可以获得用户已评分物品之间的相关性;同时由于不同的用户有不同的评分习惯,新方法也考虑了每个用户的评分偏好。通过考虑用户相似性的更多因素,可以为目标用户选择更恰当的邻域用户,以更有效地提升推荐性能。在两个真实数据集上进行的实验表明,所提方法优于其他当前最好的相似度方法。

关键词: 协同过滤,Bhattacharyya系数,项目相关性,评分偏好

Abstract: In order to satisfy the information needs of users in the big data era,the personalized recommender system has been widely used.Collaborative filtering is a simple and effective recommendation algorithm.However,most traditional similarity methods only compute the similarity based on the users’ co-rated scores.In addition,they are not very suitable in sparse data environment.This paper proposed a new similarity method based on Bhattacharyya coefficient.It uses all users’ rating information for items,which can not only obtain similar interest feature of users through the user’srating behavior,but also obtain the correlation between the items that the users have rated.Meanwhile,the new me-thod also takes into account each user’s rating preference,since different users have different rating habits.Considering more relevant factor about user similarity,more appropriate neighborhood can be selected for the target users,efficiently improving the recommendations.With experiments on two real data sets,the results show that our method outperforms the other state-of-the-art similarity metrics.

Key words: Cllaborative filtering,Bhattacharyya coefficient,Item correlation,User preference

[1] CECHINEL C,SICILIA M ,SNCHEZ-ALONSO S,et al.Evaluating collaborative filtering recommendations inside large learning object repositories[J].Inf.Process.Manag., 2013,49(1):34-50.
[2] PAZZANI M J,BILLSUS D.Content-based recommendationsystems[C]∥The Adap.Web..2007:325-341.
[3] BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Procee-dings of the Fourteenth Conference on Uncertainty in Artificial Intelligence.1998:43-52
[4] BURKE R.Integrating knowledge-based and collaborative-filtering recom-mender systems[C]∥Proc.Work.AI Electron.Commer..1999:69-72.
[5] LIU Z,QU W,LI H,et al.A hybrid collaborative filteringrecommen-dation mechanism for P2P networks[J].Future Gene-ration Computer Systems,2010,26(8):1409-1417.
[6] BREESE J S, HECKERMAN D,KADIE C.Emprical Analysis of Predictive algorithm for Collaborative filtering[J].Fourteenth Conference on Uncertainty in Artificial Intelligence,1998,7(7):43-52.
[7] DENG A L,ZHU Y Y,SHI B L.A collaborative filtering recom-mendation algorithm based on item rating prediction[J].Joumal of Software,2003,4(9):1621-1628.(in Chinese) 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,4(9):1621-1628.
[8] GOLDBERG K,ROEDER T,GUPTA G,et al.Eigentaste:aconstant time collaborative filtering algorithm[J].Information Retrieval,2001,4(2):133-151.
[9] SARWAR B M,KARPIS G,KONSTAN J A,et al.Application of dimen-sionality reduction in recommender system-a case study[M]∥ACM WebKDD Workshop.2000
[10] BEDI P,KAUR H,MARWAHA S.Trust based recommender system for semantic Web[C]∥Proc.of IJCAI’07.2007:2677-2682.
[11] MA H,KING I,LYU M R.Learning torecommend with social trust ensemble[C]∥Proc.of SIGIR’09.Boston,MA,USA,2009:203-210.
[12] MA H,YANG H,LYU M R,et al.SoRec:Social recommendation using probabilistic matrix factorization[C]∥Proceedings of CIKM ’08.Napa Valley,USA,2008:931-940.
[13] MASSA P,AVESANI P.Trust-aware recommender systems[C]∥Proc.of RecSys ’07.Minneapolis,MN,USA,2007:17-24.
[14] RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]∥Proceeding of the ACM Conference on Computer Supported Cooperative Work.1994:175-186
[15] SHI Y,LARSON M,HANJALIC A.Collaborative filtering beyond the user-item matrix:A survey of the state of the art and future challenges[J].ACM Computing Surveys (CSUR),2014,47(1):1-45
[16] CACHEDA F,CARNEIRO V,FERNNDEZ D,et al.Comparison of collaborative filtering algorithms:limitations of current techniques and proposals for scalable,high-performance recommender system[J].ACM Transactions Web,2011,5(1):1-33.
[17] HUANG C G,YIN J,WANG J,et al.Uncertain neighbors’ collaborative filtering recommendation algorithm[J].Chinese Journal of Computers,2010,3(87):1369-1377.(in Chinese) 黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,3(87):1369-1377.
[18] LUO X,OUYANG Y X,XIONG Z,et al.The effect of similarity support in K-nearest-neighborhood based collaborative filtering[J].Chinese Journal of Computers,2010,3(8):1437-1455.(in Chinese) 罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,3(8):1437-1455.
[19] XING C X,GAO F R,ZHAN S A,et al.A collaborative filtering recommendation algorithm in corporate with user interest change [J].Journal of Computer Research and Development,2007,4(2):296-301.(in Chinese) 邢春晓,高凤荣,战思南,等.适应用户兴趣变化的协同过滤推荐算法[J].计算机研究与发展,2007,4(2):296-301.
[20] STREHL A,GHOSH J,MOONEY R.Impact of similaritymeasures on web-page clustering[C]∥Proceedings of the International Workshop on Artificial Intelligence for Web Search.2000:58-64.
[21] HERLOCKER J L,KONSTAN J A,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]∥Proceedings of the Twenty Second Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,1999:230-237.
[22] JAMALI M,ESTER M.Trustwalker:A random walk model for combining trust-based and item-based recommendation[C]∥Proceedings of the fifteenth ACM SIGKDD international conferen-ce on knowledge discovery and data mining.ACM,2009:397-406
[23] SHARDANAND U,MAES P.Social information filtering:algorithms for automating word of mouth[C]∥Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.1994:210-217.
[24] POLATIDS N,GEORGIADIS C K.A multi-level collaborative filtering method that improves recommendations[J].Expert Systems with Applications,2016,48:100-110.
[25] LU J,SHAMBOUR Q,XU Y,et al.A web-based personalized business partner recommendation system using fuzzy semantic techniques[J].Computational Intelligence,2013,29(1):37-69.
[26] LUO H,NIU C,SHEN R,et al.A collaborative filtering framework based on both local user similarity and global user similarity[J].Mach.Learn.,2008,72(3):231-245.
[27] LIU H,HU Z,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Systems,2014,56(3):156-166.
[28] WANG W,ZHANG G,LU J.Collaborative filtering with entropy-driven user similarity in recommender systems[J].International Journal of Intelligent Systems,2015,30(8):854-870.
[29] BHATTACHARYYA A.On a measure of divergence between two statistical populations defined by their probability distributions[J].Bull.Calcutta Math.Soc.,1943,5(1):99-109.
[30] KAILATH T.The divergence and Bhattacharyya distance mea-sures in signal selection[J].IEEE Transactions Commun.Technol.,1967,15(1):52-60.
[31] NIELSEN F,BOLTZ S.The Burbea-Rao and Bhattacharyya centroids[J].IEEE Transactions Inf.Theory,2011,57(8):5455-5466.
[32] AHERNE F J,THACKER N A,ROCKETT P.The Bhattacharyya metric as an absolute similarity measure for frequency coded data[J].Kybernetika,1998,34(4):363-368.
[33] HUANG A.Similarity measures for text document clustering[C]∥Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008).Christchurch,New Zealand,2008:49-56.

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