|Table of Contents|

Design and Implementation of Item-Based Parallel Collaborative Filtering Algorithm(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2014年01期
Page:
71-
Research Field:
计算机科学
Publishing date:

Info

Title:
Design and Implementation of Item-Based Parallel Collaborative Filtering Algorithm
Author(s):
Yan CunJi Genlin
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
recommendaton systemcollaborative filterinigHadoopMapReduce
PACS:
TP311
DOI:
-
Abstract:
Collaboration filtering has been widely used in recommender system.However,with the increase of user numbers and item numbers,the computing ability of single computer can not satisfy the requirement of real-time performance and the requirement of scalability when computing the similarity and prediction.To address the issue,this paper presents an Item-Based parallel collaborative filtering recommendation algorithm.Adopting the framework of MapReduce and HDFS in Hadoop,the parallel algorithm is divided into two procedures,which are Map and Reduce.Through the computation in parallel in respective Map and Reduce nodes,the algorithm performance is improved.The experimental results show that the proposed algorithm can decrease the recommend time and has a good real-time performance and scalability.

References:

[1] Schafer J B,Konstan J A,Riedl J.E-commerce recommendation applications[J].Data Mining and Knowledge Discovery,2001,5(1/2):115-153.
[2]Linden G,Smith B,York J.Amazeon.com recommendations:item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[3]O’Conner M,Herlocker J.Clustering items for collaborative filtering[C]//Proceeding of the ACM SIGIR Workshop on Recommender System.California:UC Berkeley,1999:121-128.
[4]李忠俊,周启海,帅青红.一种基于内容和协同过滤同构化整合的推荐系统模型[J].计算机科学,2009,36(12):142-145.
[5]Lee J S,Jun C H,Kim S H.Mining changes in customer buying behavior for collaborative recommendations[J].Expert System with Applications,2005,29(3):700-704.
[6]Schafer J B,Frankowski D,Herlocker J,et al.Collaborative Filtering Recommender Systems[M].Berlin Heidelberg:Springer,2007:291-324.
[7]Sarwer B,Karypis G,Konstan J,et al.Item-based collaborative filtering recommendation algorithms[C]//Proceeding of the 10th International Conference on World Wide Web.Hong Kong:ACM Press,2001:285-295.
[8]Zhao Z D,Shang M S.User-based collaborative-filtering recommendation algorithms on hadoop[C]//Third International Conference on Knowledge Discovery and Data Mining.Thailand:IEEE,2010:478-481.
[9]Borthakur D,Gray J,Sarma J S,et al.Apache hadoop goes realtime at Facebook[C]//Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data.Athens:ACM,2011:1 071-1 080.
[10]Jiang J,Lu J,Zhang G,et al.Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop[C]//2011 IEEE World Congress on Services(SERVICES).Washington:IEEE,2011:490-497.
[11]Dean J,Ghemawat S.MapReduce:simplified data processing on large clusters[J].Communications of the ACM,2008,51(1):107-113.
[12]Shvachko K,Kuang H,Radia S,et al.The hadoop distributed file system[C]//2010 IEEE 26th Symposium on Mass Storage Systems and Technologies.Nevada:IEEE,2010:1-10.
[13]Ahn H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.

Memo

Memo:
-
Last Update: 2014-03-30