Resources

Visit our lab page on ResearchGate, here.

 

Recommender Systems course at Politecnico di Milano

This repository contains the materials provided to our students of the Recommender Systems course at Politecnico di Milano, 2018.

The materials are accessible HERE.

 

 

Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches

The article has been published in RecSys 2019, the full text is accessible onĀ ACM Digital Library, ArXiv or ResearchGate, the Github repository HERE.

Please cite our article if you use our repository or algorithms.

@Article{Ferraridacrema2019,
author={Ferrari Dacrema, Maurizio
and Cremonesi, Paolo
and Jannach, Dietmar},
title={Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches},
journal={Proceedings of the 13th ACM Conference on Recommender Systems (RecSys 2019)},
year={2019},
doi={10.1145/3298689.3347058},
Eprint={arXiv:1907.06902},
note={Source: \url{https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation}},
}

 

Collaborative Filtering enhanced Content Based Filtering

This repository contains the core model we called “Collaborative filtering enhanced Content-based Filtering” published in our UMUAI article “Movie Genome: Alleviating New Item Cold Start in Movie Recommendation

The article is accessible HERE, the Github repository HERE.

Please cite our article if you use this repository or algorithm.

@Article{Deldjoo2019,
author="Deldjoo, Yashar and Dacrema, Maurizio Ferrari and Constantin, Mihai Gabriel and Eghbal-zadeh, Hamid
and Cereda, Stefano and Schedl, Markus and Ionescu, Bogdan and Cremonesi, Paolo",
title="Movie genome: alleviating new item cold start in movie recommendation",
journal="User Modeling and User-Adapted Interaction",
year="2019",
month="Feb",
day="26",
issn="1573-1391",
doi="10.1007/s11257-019-09221-y",
url="https://doi.org/10.1007/s11257-019-09221-y",
note="Source: \url{https://github.com/MaurizioFD/CFeCBF}"
}