|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 Cun; Ji Genlin
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
recommendaton system; collaborative filterinig; Hadoop; MapReduce
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:

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Last Update: 2014-03-30