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

The finite section method for infinite Vandermonde matrices

AbstractThe finite section method for infinite Vandermonde matrices is the focus of this paper. In particular, it is shown that for a large class of infinite Vandermonde matrices the finite section method converges in l1 sense if the right hand side of the equation is in a suitably weighted l1(α) space. Some explicit results are obtained for a wide class of examples