424 citations · 855 across the 27 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…