Paper Info
Title
MapReduce performance evaluation for knowledge-based recommendation of context-tagged photos
Abstract
Recommendation systems are a subclass of information filtering systems that aims at helping users in retrieving information. Recently, contextual information proved to be effective in improving the quality of results of Recommender Systems. However, Context-aware Recommender Systems still suffer performance issues for real-time recommendation, mainly due to the amount of items that should be considered for recommendation. In this paper, we present an evaluation of using MapReduce and its integration with a mobile system for implementing a knowledge-based algorithm for context-aware recommendation. To be effective, this photo recommendation algorithm should work with a large set of images annotated with contextual information. The MapReduce algorithm parallelizes the processing required to generate the recommendation results and so improved the system performance. The results of performance analysis showed, for instance, that cloud-based version of the reccomendation reaches a speedup of 7x with a image base with more than 41 million photos.
Year
DOI
Venue
2013
10.1145/2526188.2530537
WebMedia
Keywords
Field
DocType
photo recommendation algorithm,recommendation system,recommendation result,context-tagged photo,contextual information,mapreduce algorithm,knowledge-based recommendation,real-time recommendation,performance analysis,mapreduce performance evaluation,knowledge-based algorithm,retrieving information,context-aware recommendation,recommender systems
Mobile cloud computing,Recommender system,Data mining,Quality of results,Contextual information,Information retrieval,Computer science,Filter (signal processing),Speedup
Conference
Citations 
PageRank 
References 
1
0.36
11
Authors
5
Name
Order
Citations
PageRank
Paulo Antonio Leal Rego1659.32
Fabrício D.A. Lemos251.18
Windson Viana320128.40
Fernando Trinta43914.93
José Neuman de Souza549364.45