We study the problem of recommending relevant products to users in relatively resource-
scarce markets by leveraging data from similar, richer in resource auxiliary markets. We …

As more and more novel algorithms being proposed, it is difficult for engineers to choose
suitable algorithms to solve engineering problems in manufacturing systems. According to …

While other areas of machine learning have seen more and more automation, designing a
high-performing recommender system still requires a high level of human effort …

We introduce the first 'living lab'for scholarly recommender systems. This lab allows
recommender-system researchers to conduct online evaluations of their novel algorithms for …

Purpose This paper aims to describe the use of a meta-learning framework for
recommending cost-sensitive classification methods with the aim of answering an important …

Recommendation algorithms perform differently if the users, recommendation contexts,
applications, and user interfaces vary even slightly. It is similarly observed in other fields …

Abstract “Federated Meta-Learning”(FML), a concept that allows everyone to benefit from the
data that is generated through software libraries including machine learning and data …

The main contribution of this paper is to introduce and describe a new recommender-
systems dataset (RARD II). It is based on data from Mr. DLib, a recommender-system as-a …

Cluster-based algorithm selection deals with selecting recommendation algorithms on
clusters of users to obtain performance gains. No studies have been attempted for many …

The DonorsChoose. org dataset of past donations provides a big and feature-rich corpus of
users and items. The dataset matches donors to projects in which they might be interested in …