424 citations · 855 across the 27 of their papers we have counts for
28 papers
Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling
Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema +1
Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly robust (DR) estimation remai…
Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability
F. Picariello, G. Turati, R. Antonelli +10
The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, widely applied in logistics and transportation. As the size of TSP instances grows, t…
Minor Embedding for Quantum Annealing with Reinforcement Learning
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Quantum Annealing (QA) is a quantum computing paradigm for solving combinatorial optimization problems formulated as Quadratic Unconstrained Binary Optimization (QUBO) problems. An…
Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
Filippo Brozzi, Gloria Turati, Maurizio Ferrari Dacrema +2
In the context of Variational Quantum Algorithms (VQAs), selecting an appropriate ansatz is crucial for efficient problem-solving. Hamiltonian expressibility has been introduced as…
Comment on arXiv:2307.08384 "Efficient Quantum State Preparation with Walsh Series"
Riccardo Pellini, Maurizio Ferrari Dacrema
In this paper, we discuss the Walsh Series Loader (WSL) algorithm, proposed by. In particular, we observe that the paper does not describe how to implement the term of order zero o…
A Worrying Reproducibility Study of Intent-Aware Recommendation Models
Faisal Shehzad, Maurizio Ferrari Dacrema, Dietmar Jannach
Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by…
An Empirical Analysis on the Effectiveness of the Variational Quantum Linear Solver
Gloria Turati, Alessia Marruzzo, Maurizio Ferrari Dacrema +1
Variational Quantum Algorithms (VQAs) have emerged as promising methods for tackling complex problems on near-term quantum devices. Among these algorithms, the Variational Quantum…
Reinforcement Learning for Variational Quantum Circuits Design
Simone Foderà, Gloria Turati, Riccardo Nembrini +2
Variational Quantum Algorithms have emerged as promising tools for solving optimization problems on quantum computers. These algorithms leverage a parametric quantum circuit called…
Adaptive Learning for Quantum Linear Regression
Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi
The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to…
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning
Riccardo Pellini, Maurizio Ferrari Dacrema
The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in p…
Automated Off-Policy Estimator Selection via Supervised Learning
Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema
The Off-Policy Evaluation (OPE) problem consists of evaluating the performance of counterfactual policies with data collected by another one. To solve the OPE problem, we resort to…
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one lim…
Impression-Aware Recommender Systems
Fernando B. Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells +1
Novel data sources bring new opportunities to improve the quality of recommender systems and serve as a catalyst for the creation of new paradigms on personalized recommendations.…
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…
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
This work explores the reproducibility of CFGAN. CFGAN and its family of models (TagRec, MTPR, and CRGAN) learn to generate personalized and fake-but-realistic rankings of preferen…
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…
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario
Sebastiano Antenucci, Simone Boglio, Emanuele Chioso +4
In this paper we provide an overview of the approach we used as team Creamy Fireflies for the ACM RecSys Challenge 2018. The competition, organized by Spotify, focuses on the probl…
ContentWise Impressions: An Industrial Dataset with Impressions Included
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule +2
In this article, we introduce the ContentWise Impressions dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service,…
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi +1
In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However…
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi +1
The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, appr…
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features
Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema +2
In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build…