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Recommender systems have widely grown thanks to their outstanding capability of providing users with accurate and tailored recommendations. However, recently, public awareness about data property and new regulations like GDPR forced recommender system researchers and practitioners to study solutions to user privacy endangerment. In this context, this year at RecSys, the premier international forum on […]

ELLIOT is a novel kind of recommendation framework aimed to overcome obstacles about reproducibility in the RecSys field by proposing a fully declarative approach(using a configuration file) to the set-up of an experimental setting. It analyzes the recommendation problem from the researcher’s perspective as it implements the whole experimental pipeline, from dataset loading to results […]