Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Future Generation Computer Systems
Context awareness by case-based reasoning in a music recommendation system
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop
SERVICES '11 Proceedings of the 2011 IEEE World Congress on Services
Scalable similarity-based neighborhood methods with MapReduce
Proceedings of the sixth ACM conference on Recommender systems
Improving photo recommendation with context awareness
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Mobile cloud computing: A survey
Future Generation Computer Systems
Designing a Collaborative Filtering Recommender on the Single Chip Cloud Computer
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
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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.