Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 428-435.doi: 10.11896/j.issn.1002-137X.2016.11A.097

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Distributed Collaborative Filtering Recommendation Based on User Co-occurrence Matrix Multiplier

HE Ming, WU Xiao-fei, CHANG Meng-meng and REN Wan-peng   

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

Abstract: With the era of big data’s coming,the amount of application data increases sharply,the result of which gives more and more prominence to a personalized recommendation technique.However,traditional recommendation techniques applied to big data are confronted with some problems,such as low recommendation accuracy,long recommendation time and high network traffic.Therefore,the performance of recommendation degrades drastically.To address this issue,a user co-occurrence matrix recommendation strategy was proposed in this paper. The user for the project’s predi-cation rating matrix is got by multiplying user similarity matrix and item rating matrix.Candidate items set is generated for each user using user similarity matrix multiply by item similarity matrix.On this basis,traditional collaborative filtering algorithms were parallel expand according to the feature of distributed processing architecture,and a distributed collaborative filtering algorithm was designed.Finally,multi-sub tasks are in series utilizing combination of redefined MapReduce schema to execute automatically.Experimental results show that our approach achieve better prediction accuracy and efficiency in distribute computing environment.

Key words: Collaborative filtering,Recommendation systems,Distributed computing,Big data

[1] Resnick P,Varian H R.Recommender Systems [J].Communications of the ACM,1997,40(3):56-58
[2] Goldberg D,Nicholas D,Oki B M,et al.Using Collaborative Filtering to Weave an Information Tapestry [J].Communications of the ACM,1992,35(12):61-70
[3] Adomavicius G,Tuzhilin A.Toward the next generation of recom-mender systems:A survey of the state-of-the-art and possible extensions [J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749
[4] Manzato M,Goularte R.Amultimedia recommender systembased on enriched user profiles [C]∥Proc of the 27th Annual ACM Symposium on Applied Computing.New York:ACM,2012:975-980
[5] 孟晓峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,0(1):146-169
[6] Hadoop [EB/OL].[2016-05-18].
[7] Chiky R,Ghisloti R,Kazi-Aoul Z.Development of a distributed recommender system using the Hadoop framework [C]∥Proc of EGC.2012:495-500
[8] Jiang Jing,Lu Jie,Zhang Guang-quan,et al.Scaling-up item-based collaborative filtering recommendation algorithm based on Hadoop [C]∥Proc of SERVICES 2011.Piscataway,NJ:IEEE,2011:490-497
[9] De Pesemier T,Vanheke K,Dooms S,et al.Content-based recom-mendation algorithms on the Hadoop MapReduce framework [C]∥Proc of WEBIST 2011.New York:ACM,2011:237-240
[10] Kupisz B,Unold O.Collaborative filtering recommendation algorithm based on Hadoop and Spark[C]∥IEEE International Conference on Industrial Technology.IEEE,2015:1510-1514
[11] Fan L,Li H,Li C.The improvement and implementation of distributed item-based collaborative filtering algorithm on Hadoop[C]∥Proc of 34th Chinese Control Conference.IEEE,2015:9078-9083
[12] Dean J,Ghemawat S.MapReduce:Simplified data processing on large clusters[C]∥Porc of OSDI 2004.Berkeley,CA:USENIX Association,2004:137-150
[13] 冯璐,冷伏海.共词分析方法理论进展[J].中国图书馆学报,2006:32(2):88-92
[14] Breeese J S,Hecherman D,Kadie C.Empirical analysis of predictive algorithms for collaborative filtering [C]∥Proc of 14th conference on Uncertainty in artificial intelligence.San Francisco:Morgan Kaufmann,1998:43-52

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