21 research outputs found
Idomaar : a framework for multi-dimensional benchmarking of recommender algorithms
In real-world scenarios, recommenders face non-functional requirements of technical nature and must handle dynamic data in the form
of sequential streams. Evaluation of recommender systems must
take these issues into account in order to be maximally informative.
In this paper, we present Idomaar—a framework that enables the
efficient multi-dimensional benchmarking of recommender algorithms. Idomaar goes beyond current academic research practices
by creating a realistic evaluation environment and computing both
effectiveness and technical metrics for stream-based as well as set-based evaluation. A scenario focussing on “research to prototyping
to productization” cycle at a company illustrates Idomaar’s potential.
We show that Idomaar simplifies testing with varying configurations
and supports flexible integration of different data
Overview of NewsREEL’16: Multi-dimensional evaluation of real-time stream-recommendation algorithms
Successful news recommendation requires facing the challenges of dynamic item sets, contextual item relevance, and of fulfilling non-functional requirements, such as response time. The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to tackle news recommendation and to optimize and evaluate their recommender algorithms both online and offline. In this paper, we summarize the objectives and challenges of NewsREEL 2016. We cover two contrasting perspectives on the challenge: that of the operator (the business providing recommendations) and that of the challenge participant (the researchers developing recommender algorithms). In the intersection of these perspectives, new insights can be gained on how to effectively evaluate real-time stream recommendation algorithms