MapReduce performance evaluation for knowledge-based recommendation of context-tagged photos

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.