Idomaar

Abstract

In real-world scenarios, recommenders face non-functional requirementsof technical nature and must handle dynamic data in the formof sequential streams. Evaluation of recommender systems musttake these issues into account in order to be maximally informative.In this paper, we present Idomaar—a framework that enables theefficient multi-dimensional benchmarking of recommender algorithms.Idomaar goes beyond current academic research practicesby creating a realistic evaluation environment and computing botheffectiveness and technical metrics for stream-based as well as setbasedevaluation. A scenario focussing on “research to prototypingto productization” cycle at a company illustrates Idomaar’s potential.We show that Idomaar simplifies testing with varying configurationsand supports flexible integration of different data.