Name
Abstract | ||
|---|---|---|
Recommendation systems are important big data applications that are used in many business sectors of the global economy. While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the high-performance shared-memory MapReduce system Phoenix++ [1] to design a faster recommendation engine. In this paper, we design a distributed out-of-core recommendation algorithm to maximize the usage of main memory, and devise a framework that invokes Phoenix++ as a sub-module to achieve high performance. The design of the framework can be extended to support different types of big data applications. The experiments on Amazon Elastic Compute Cloud (Amazon EC2) demonstrate that our new recommendation system can be faster than its Hadoop counterpart by up to 225% without losing recommendation quality. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICITST.2013.6750200 | ICITST |
Keywords | Field | DocType |
parallel programming,high-performance recommendation system,recommender systems,distributed out-of-core recommendation algorithm,phoenix++,big data applications,amazon elastic compute cloud,big data,hadoop-like mapreduce systems,motion pictures,sparse matrices,internet,prediction algorithms,scalability,collaboration,clustering algorithms | Recommender system,Data mining,Computer science,Computer network,Cluster analysis,Phoenix,Big data,Sparse matrix,Database,Cloud computing,The Internet,Scalability | Conference |
ISSN | Citations | PageRank |
2164-7046 | 2 | 0.39 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chongxiao Cao | 1 | 2 | 0.39 |
| Fengguang Song | 2 | 232 | 19.88 |
| Daniel G. Waddington | 3 | 27 | 3.55 |