DocumentCode :
3006489
Title :
MapReduce-Based SimRank Computation and Its Application in Social Recommender System
Author :
Lina Li ; Cuiping Li ; Hong Chen ; Xiaoyong Du
Author_Institution :
Key Lab. of Data Eng. & Knowledge Eng., Renmin Univ. of China, Beijing, China
fYear :
2013
fDate :
June 27 2013-July 2 2013
Firstpage :
133
Lastpage :
140
Abstract :
Recently there has been a lot of interest in graph-based analysis, with examples including social network analysis, recommendation systems, document classification and clustering, and so on. A graph is an abstraction that naturally captures data objects as well as relationships among those objects. Objects are represented as nodes and relationships are represented as edges in the graph. There are many cases in which similarities among nodes are required to compute. SimRank is one of the simple and intuitive algorithms for this purpose. It is rigidly based on the random walk theorem. Existing methods on SimRank computation suffer from one limitation: the computing cost can be very high in practice. In order to optimize the computation of SimRank, a few techniques have been proposed. However, the performance of these methods are still limited by the processing ability of the single computer. Ideally, we would like to develop new parallel solutions that can offer improved processing power to compute SimRank on large data set. In this paper, we propose parallel algorithms for SimRank computation on Map-Reduce framework, and more specifically its open source implementation, Hadoop. Two different parallel methods are proposed and their performances are evaluated and compared. Furthermore, we employ the proposed methods to do the similarity computation in order to recommend appropriate products to users in social recommender systems.
Keywords :
graph theory; parallel algorithms; recommender systems; social networking (online); Hadoop; MapReduce-based SimRank computation; data objects; graph-based analysis; object relationship; open source implementation; parallel algorithms; parallel solutions; random walk theorem; similarity computation; social recommender system; Computational modeling; Computers; Equations; Iterative methods; Parallel processing; Recommender systems; Sorting; Mapreduce; Parallel; Recommendation; Simrank;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
Type :
conf
DOI :
10.1109/BigData.Congress.2013.26
Filename :
6597129
Link To Document :
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