A scholarly recommendation system is an important tool for identifying prior and related
resources such as literature, datasets, grants, and collaborators. A well-designed scholarly …

We propose an approach to match returning donors to fundraising campaigns on
philanthropic crowdfunding platforms. It is based on a structural econometric model of utility …

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 …

The recommender systems algorithm selection problem for ranking prediction on implicit
feedback datasets is under-explored. Traditional approaches in recommender systems …

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

The difficulty in finding the most suitable recommendation algorithm for all requests is a
common challenge in the recommendation system context, regardless of the domain …

Automated per-instance algorithm selection often outperforms single learners. Key to
algorithm selection via meta-learning is often the (meta) features, which sometimes though …

Despite the proliferation of recommendation algorithms, the question of which recommender
works best for which user-item instance remains widely open. In this paper, we develop a …

There is an ever-growing number of tools for automating the machine learning pipeline, both
commercial and open source. Auto-sklearn [11, 15], Auto Weka [14], ML-Plan [18], and H2O …

Les données des utilisateurs deviennent de plus en plus disponibles dans plusieurs
domaines, allant des plateformes de commerce électronique aux réseaux sociaux. Elles …