584 citations · 1.5k across the 28 of their papers we have counts for
29 papers
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…
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-…
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…
On the instability of embeddings for recommender systems: the case of Matrix Factorization
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis +1
Most state-of-the-art top-N collaborative recommender systems work by learning embeddings to jointly represent users and items. Learned embeddings are considered to be effective to…
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 Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations
Stefano Cereda, Gianluca Palermo, Paolo Cremonesi +1
Selecting the right compiler optimisations has a severe impact on programs' performance. Still, the available optimisations keep increasing, and their effect depends on the specifi…
Introducing a framework to assess newly created questions with Natural Language Processing
Luca Benedetto, Andrea Cappelli, Roberto Turrin +1
Statistical models such as those derived from Item Response Theory (IRT) enable the assessment of students on a specific subject, which can be useful for several purposes (e.g., le…
R2DE: a NLP approach to estimating IRT parameters of newly generated questions
Luca Benedetto, Andrea Cappelli, Roberto Turrin +1
The main objective of exams consists in performing an assessment of students' expertise on a specific subject. Such expertise, also referred to as skill or knowledge level, can the…
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering
Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the m…
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…
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine lea…
A Virtual Teaching Assistant for Personalized Learning
Luca Benedetto, Paolo Cremonesi, Manuel Parenti
In this extended abstract, we propose an intelligent system that can be used as a Personalized Virtual Teaching Assistant (PVTA) to improve the students learning experience both fo…
Deriving item features relevance from collaborative domain knowledge
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based f…
Eigenvalue analogy for confidence estimation in item-based recommender systems
Maurizio Ferrari Dacrema, Paolo Cremonesi
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of…
A novel graph-based model for hybrid recommendations in cold-start scenarios
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not appli…
Sequence-Aware Recommender Systems
Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often bas…