Lior Rokach

Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. Recommender systems have proven to be a valuable means for online users to cope with the virtual information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed during the last decade. In this paper we present a new benchmark framework. It allows researchers or practitioners to quickly try out and compare different recommendation methods on new data sets. Extending the framework is easy thanks to a simple and well-defined Application Programming Interface (API). It contains a plug-in mechanism allowing others to develop their own algorithms and incorporate them in the framework. An interactive graphical user interface is provided for setting new benchmarks, integrate new plug-ins with the framework, setting up configurations and exploring benchmark results.