activity
20172020
most citedContent Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report

2 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.IR2020

Predict your Click-out: Modeling User-Item Interactions and Session Actions in an Ensemble Learning Fashion

Andrea Fiandro, Giorgio Crepaldi, Diego Monti +2

This paper describes the solution of the POLINKS team to the RecSys Challenge 2019 that focuses on the task of predicting the last click-out in a session-based interaction. We prop…

cs.IR2019

All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation

Diego Monti, Giuseppe Rizzo, Maurizio Morisio

The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems. However, the numbe…

cs.SI2018

Semantic Trails of City Explorations: How Do We Live a City

Diego Monti, Enrico Palumbo, Giuseppe Rizzo +3

The knowledge of city exploration trails of people is in short supply because of the complexity in defining meaningful trails representative of individual behaviours and in the acc…

cs.IR2018

Sequeval: A Framework to Assess and Benchmark Sequence-based Recommender Systems

Diego Monti, Enrico Palumbo, Giuseppe Rizzo +1

In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items. A sequence-based r…

cs.IR2018

A Distributed and Accountable Approach to Offline Recommender Systems Evaluation

Diego Monti, Giuseppe Rizzo, Maurizio Morisio

Different software tools have been developed with the purpose of performing offline evaluations of recommender systems. However, the results obtained with these tools may be not di…

cs.IR20172 cited

Content Recommendation through Semantic Annotation of User Reviews and Linked Data - An Extended Technical Report

Iacopo Vagliano, Diego Monti, Ansgar Scherp +1

Nowadays, most recommender systems exploit user-provided ratings to infer their preferences. However, the growing popularity of social and e-commerce websites has encouraged users…