Implementation of context-aware Item recommendation through MapReduce data aggregation

Autoren Wolfgang Beer
Christian Derwein
Sandor Herramhof
Editoren
TitelImplementation of context-aware Item recommendation through MapReduce data aggregation
BuchtitelProceedings of the 11th International Conference on Advances in Mobile Computing & Multimedia - MoMM 2013
Typin Konferenzband
VerlagACM
ISBN978-1-4503-2106-8
MonatDecember
Jahr2013
Seiten26-32
SCCH ID#1361
Abstract

As the amount of ubiquitous product and service information within our daily lives is exploding, client-centric and context-aware information filtering is one of the thriving topics within the next years. A popular approach is to combine context-awareness with traditional recommendation engines in order to evaluate the relevance of a large amount of items for a given situation and user. Within this work we propose a general software architecture as well as a prototypical implementation for a framework that combines traditional recommendation methods with a variable number of context dimensions, such as location of social context. This work shows how to use a MapReduce programming model for aggregating the necessary information for calculating fast context-aware recommendations. A use-case at the end of this work shows how to use this general framework to implement a client-centric, MapReduce-based recommendation engine for real-time recommending music events.