584 citations · 796 across the 13 of their papers we have counts for
23 papers
Feature Selection for Classification with QAOA
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of r…
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini +3
Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking o…
GAN-based Matrix Factorization for Recommender Systems
Ervin Dervishaj, Paolo Cremonesi
Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in generative modelling. They immediately achieved state-of-the-art in image synthesis, image-to-…
Feature Selection for Recommender Systems with Quantum Computing
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum comp…
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists, i.e. widgets or swipeable carousels, each generate…
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e. widgets or swipeable carousels, each built with a sp…