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cfecbf

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"

contentwise-impressions

This repository contains the code used to run generate the data splits, run the hyperparameter tunings, and export the results presented in our article "ContentWise Impressions: An industrial dataset with impressions included"

ecir-2022-an-evaluation-of-gan-for-cf

This repository contains the source code and data used in our experiments described in the paper "An evaluation of Generative Adversarial Networks for Collaborative Filtering". Refer to the README file to run our experiments.

iir2022

IIR 2022 | 12th Italian Information Retrieval Workshop

leri2023

LERI 2023 | Workshop on Learning and Evaluating Recommendations with Impressions @ RecSys 2023

neurips-2022-ope-side-info

This repository contains the code for our paper "Off-Policy Evaluation with Deficient Support Using Side Information", accepted at NeurIPS 2022.

recsys-2022-evaluation-of-recsys-with-impressions

recsys-challenge-2019-trivago

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019 by our team Policloud8 at Politecnico di Milano

recsys-challenge-2020-twitter

The complete code and notebooks used for the ACM Recommender Systems Challenge 2020 by our team BanaNeverAlone at Politecnico di Milano

recsys-challenge-2021-twitter

The complete code and notebooks used for the ACM Recommender Systems Challenge 2021 by our team Trial&Error at Politecnico di Milano

recsys-challenge-2022-dressipi

The complete code and notebooks used for the ACM Recommender Systems Challenge 2022 by our team Boston Tea Party at Politecnico di Milano

recsys-challenge-2023-sharechat

The complete code and notebooks used for the ACM Recommender Systems Challenge 2023 by our team Gabibboost at Politecnico di Milano

recsys2019_deeplearning_evaluation

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

recsys_course_at_polimi

This is the official repository for the Recommender Systems course at Politecnico di Milano.

recsyscarouselevaluation

This repository contains the code for the paper "A Methodology for the Offline Evaluation of RecommenderSystems in a User Interface with Multiple Carousels", published at UMAP Late-Breaking Results 2021.

spotify-recsys-challenge

A complete set of Recommender Systems techniques used in the Spotify Recsys Challenge 2018 developed by a team of MSc students in Politecnico di Milano.