Case Study Evaluation of Mahout as a Recommender Platform
Carlos E. Seminario, David C. Wilson · 2012
Various libraries have been released to support the development of recommender systems for some time, but it is only relatively recently that larger scale, open-source platforms have become readily available. In the context of such platforms, evaluation tools are important both to verify and validate baseline platform functionality, as well as to provide support for testing new techniques and approaches developed on top of the platform. We have adopted Apache Mahout as an enabling platform for our research and have faced both of these issues in employing it as part of our work in collaborative filtering. This paper presents a case study of evaluation focusing on accuracy and coverage evaluation metrics in Apache Mahout, a recent platform tool that provides support for recommender system application development. As part of this case study, we developed a new metric combining accuracy and coverage in order to evaluate functional changes made to Mahout’s collaborative filtering algorithms.